program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0"}})]
{
    func main<ios18>(tensor<fp16, [1, 128, 1, 3000]> melspectrogram_features) {
            string var_114_pad_type_0 = const()[name = string("op_114_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_114_pad_0 = const()[name = string("op_114_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> var_114_strides_0 = const()[name = string("op_114_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_114_dilations_0 = const()[name = string("op_114_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_114_groups_0 = const()[name = string("op_114_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 128, 1, 3]> var_89_to_fp16 = const()[name = string("op_89_to_fp16"), val = tensor<fp16, [1280, 128, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [1280]> var_95_to_fp16 = const()[name = string("op_95_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983168)))];
            tensor<fp16, [1, 1280, 1, 3000]> var_114_cast_fp16 = conv(bias = var_95_to_fp16, dilations = var_114_dilations_0, groups = var_114_groups_0, pad = var_114_pad_0, pad_type = var_114_pad_type_0, strides = var_114_strides_0, weight = var_89_to_fp16, x = melspectrogram_features)[name = string("op_114_cast_fp16")];
            string hidden_states_1_mode_0 = const()[name = string("hidden_states_1_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1280, 1, 3000]> hidden_states_1_cast_fp16 = gelu(mode = hidden_states_1_mode_0, x = var_114_cast_fp16)[name = string("hidden_states_1_cast_fp16")];
            string var_154_pad_type_0 = const()[name = string("op_154_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_154_pad_0 = const()[name = string("op_154_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> var_154_strides_0 = const()[name = string("op_154_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_154_dilations_0 = const()[name = string("op_154_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_154_groups_0 = const()[name = string("op_154_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 3]> var_129_to_fp16 = const()[name = string("op_129_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985792)))];
            tensor<fp16, [1280]> var_135_to_fp16 = const()[name = string("op_135_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10816256)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_154_cast_fp16 = conv(bias = var_135_to_fp16, dilations = var_154_dilations_0, groups = var_154_groups_0, pad = var_154_pad_0, pad_type = var_154_pad_type_0, strides = var_154_strides_0, weight = var_129_to_fp16, x = hidden_states_1_cast_fp16)[name = string("op_154_cast_fp16")];
            string hidden_states_3_mode_0 = const()[name = string("hidden_states_3_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_3_cast_fp16 = gelu(mode = hidden_states_3_mode_0, x = var_154_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> var_172_to_fp16 = const()[name = string("op_172_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1500]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10818880)))];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_1_cast_fp16 = add(x = hidden_states_3_cast_fp16, y = var_172_to_fp16)[name = string("inputs_1_cast_fp16")];
            int32 var_186 = const()[name = string("op_186"), val = int32(3)];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_205_to_fp16 = const()[name = string("op_205_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_205_to_fp16, x = inputs_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<fp16, [1280]> obj_1_mean_0_to_fp16 = const()[name = string("obj_1_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14658944)))];
            tensor<fp16, [1280]> obj_1_variance_0_to_fp16 = const()[name = string("obj_1_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14661568)))];
            tensor<fp16, [1280]> obj_1_gamma_0_to_fp16 = const()[name = string("obj_1_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14664192)))];
            tensor<fp16, [1280]> obj_1_beta_0_to_fp16 = const()[name = string("obj_1_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14666816)))];
            fp16 obj_1_epsilon_0_to_fp16 = const()[name = string("obj_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_1_cast_fp16 = batch_norm(beta = obj_1_beta_0_to_fp16, epsilon = obj_1_epsilon_0_to_fp16, gamma = obj_1_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_1_cast_fp16)[name = string("obj_1_cast_fp16")];
            string query_1_pad_type_0 = const()[name = string("query_1_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_1_strides_0 = const()[name = string("query_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_1_pad_0 = const()[name = string("query_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_1_dilations_0 = const()[name = string("query_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_1_groups_0 = const()[name = string("query_1_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14669440)))];
            tensor<fp16, [1280]> layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17946304)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_1_cast_fp16 = conv(bias = layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = string("query_1_cast_fp16")];
            string key_1_pad_type_0 = const()[name = string("key_1_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_1_strides_0 = const()[name = string("key_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_1_pad_0 = const()[name = string("key_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_1_dilations_0 = const()[name = string("key_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_1_groups_0 = const()[name = string("key_1_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17948928)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_1_cast_fp16 = conv(dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = string("key_1_cast_fp16")];
            string value_1_pad_type_0 = const()[name = string("value_1_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_1_strides_0 = const()[name = string("value_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_1_pad_0 = const()[name = string("value_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_1_dilations_0 = const()[name = string("value_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_1_groups_0 = const()[name = string("value_1_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21225792)))];
            tensor<fp16, [1280]> layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24502656)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_1_cast_fp16 = conv(bias = layers_0_self_attn_v_proj_bias_to_fp16, dilations = value_1_dilations_0, groups = value_1_groups_0, pad = value_1_pad_0, pad_type = value_1_pad_type_0, strides = value_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = string("value_1_cast_fp16")];
            tensor<int32, [4]> var_240 = const()[name = string("op_240"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_1_cast_fp16 = reshape(shape = var_240, x = query_1_cast_fp16)[name = string("mh_q_1_cast_fp16")];
            fp16 var_242_to_fp16 = const()[name = string("op_242_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_243_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = var_242_to_fp16)[name = string("op_243_cast_fp16")];
            tensor<int32, [4]> var_244 = const()[name = string("op_244"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_245_cast_fp16 = reshape(shape = var_244, x = key_1_cast_fp16)[name = string("op_245_cast_fp16")];
            bool mh_w_1_transpose_x_0 = const()[name = string("mh_w_1_transpose_x_0"), val = bool(true)];
            bool mh_w_1_transpose_y_0 = const()[name = string("mh_w_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_243_cast_fp16, y = var_245_cast_fp16)[name = string("mh_w_1_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_248_cast_fp16 = softmax(axis = var_186, x = mh_w_1_cast_fp16)[name = string("op_248_cast_fp16")];
            tensor<int32, [4]> var_249 = const()[name = string("op_249"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_250_cast_fp16 = reshape(shape = var_249, x = value_1_cast_fp16)[name = string("op_250_cast_fp16")];
            bool attn_1_transpose_x_0 = const()[name = string("attn_1_transpose_x_0"), val = bool(false)];
            bool attn_1_transpose_y_0 = const()[name = string("attn_1_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_250_cast_fp16, y = var_248_cast_fp16)[name = string("attn_1_cast_fp16")];
            tensor<int32, [4]> var_253 = const()[name = string("op_253"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_1_cast_fp16 = reshape(shape = var_253, x = attn_1_cast_fp16)[name = string("input_1_cast_fp16")];
            string obj_3_pad_type_0 = const()[name = string("obj_3_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_3_strides_0 = const()[name = string("obj_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_3_pad_0 = const()[name = string("obj_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_3_dilations_0 = const()[name = string("obj_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_3_groups_0 = const()[name = string("obj_3_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24505280)))];
            tensor<fp16, [1280]> layers_0_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27782144)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_3_cast_fp16 = conv(bias = layers_0_self_attn_o_proj_bias_to_fp16, dilations = obj_3_dilations_0, groups = obj_3_groups_0, pad = obj_3_pad_0, pad_type = obj_3_pad_type_0, strides = obj_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = input_1_cast_fp16)[name = string("obj_3_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = obj_3_cast_fp16)[name = string("inputs_3_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_271_to_fp16 = const()[name = string("op_271_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_271_to_fp16, x = inputs_3_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<fp16, [1280]> input_3_gamma_0_to_fp16 = const()[name = string("input_3_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784768)))];
            tensor<fp16, [1280]> input_3_beta_0_to_fp16 = const()[name = string("input_3_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27787392)))];
            fp16 input_3_epsilon_0_to_fp16 = const()[name = string("input_3_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_3_cast_fp16 = batch_norm(beta = input_3_beta_0_to_fp16, epsilon = input_3_epsilon_0_to_fp16, gamma = input_3_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_3_cast_fp16)[name = string("input_3_cast_fp16")];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_0_fc1_weight_to_fp16 = const()[name = string("layers_0_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27790016)))];
            tensor<fp16, [5120]> layers_0_fc1_bias_to_fp16 = const()[name = string("layers_0_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40897280)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_5_cast_fp16 = conv(bias = layers_0_fc1_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_0_fc1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
            string input_7_mode_0 = const()[name = string("input_7_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_7_cast_fp16 = gelu(mode = input_7_mode_0, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
            string hidden_states_5_pad_type_0 = const()[name = string("hidden_states_5_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_5_strides_0 = const()[name = string("hidden_states_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_5_pad_0 = const()[name = string("hidden_states_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_5_dilations_0 = const()[name = string("hidden_states_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_5_groups_0 = const()[name = string("hidden_states_5_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_0_fc2_weight_to_fp16 = const()[name = string("layers_0_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40907584)))];
            tensor<fp16, [1280]> layers_0_fc2_bias_to_fp16 = const()[name = string("layers_0_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54014848)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_5_cast_fp16 = conv(bias = layers_0_fc2_bias_to_fp16, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = layers_0_fc2_weight_to_fp16, x = input_7_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = hidden_states_5_cast_fp16)[name = string("inputs_5_cast_fp16")];
            int32 var_304 = const()[name = string("op_304"), val = int32(3)];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_323_to_fp16 = const()[name = string("op_323_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_323_to_fp16, x = inputs_5_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<fp16, [1280]> obj_5_gamma_0_to_fp16 = const()[name = string("obj_5_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54017472)))];
            tensor<fp16, [1280]> obj_5_beta_0_to_fp16 = const()[name = string("obj_5_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54020096)))];
            fp16 obj_5_epsilon_0_to_fp16 = const()[name = string("obj_5_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_5_cast_fp16 = batch_norm(beta = obj_5_beta_0_to_fp16, epsilon = obj_5_epsilon_0_to_fp16, gamma = obj_5_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_5_cast_fp16)[name = string("obj_5_cast_fp16")];
            string query_3_pad_type_0 = const()[name = string("query_3_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_3_strides_0 = const()[name = string("query_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_3_pad_0 = const()[name = string("query_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_3_dilations_0 = const()[name = string("query_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_3_groups_0 = const()[name = string("query_3_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54022720)))];
            tensor<fp16, [1280]> layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57299584)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_3_cast_fp16 = conv(bias = layers_1_self_attn_q_proj_bias_to_fp16, dilations = query_3_dilations_0, groups = query_3_groups_0, pad = query_3_pad_0, pad_type = query_3_pad_type_0, strides = query_3_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = obj_5_cast_fp16)[name = string("query_3_cast_fp16")];
            string key_3_pad_type_0 = const()[name = string("key_3_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_3_strides_0 = const()[name = string("key_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_3_pad_0 = const()[name = string("key_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_3_dilations_0 = const()[name = string("key_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_3_groups_0 = const()[name = string("key_3_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57302208)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_3_cast_fp16 = conv(dilations = key_3_dilations_0, groups = key_3_groups_0, pad = key_3_pad_0, pad_type = key_3_pad_type_0, strides = key_3_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = obj_5_cast_fp16)[name = string("key_3_cast_fp16")];
            string value_3_pad_type_0 = const()[name = string("value_3_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_3_strides_0 = const()[name = string("value_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_3_pad_0 = const()[name = string("value_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_3_dilations_0 = const()[name = string("value_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_3_groups_0 = const()[name = string("value_3_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60579072)))];
            tensor<fp16, [1280]> layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63855936)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_3_cast_fp16 = conv(bias = layers_1_self_attn_v_proj_bias_to_fp16, dilations = value_3_dilations_0, groups = value_3_groups_0, pad = value_3_pad_0, pad_type = value_3_pad_type_0, strides = value_3_strides_0, weight = layers_1_self_attn_v_proj_weight_to_fp16, x = obj_5_cast_fp16)[name = string("value_3_cast_fp16")];
            tensor<int32, [4]> var_358 = const()[name = string("op_358"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_3_cast_fp16 = reshape(shape = var_358, x = query_3_cast_fp16)[name = string("mh_q_3_cast_fp16")];
            fp16 var_360_to_fp16 = const()[name = string("op_360_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_361_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_360_to_fp16)[name = string("op_361_cast_fp16")];
            tensor<int32, [4]> var_362 = const()[name = string("op_362"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_363_cast_fp16 = reshape(shape = var_362, x = key_3_cast_fp16)[name = string("op_363_cast_fp16")];
            bool mh_w_3_transpose_x_0 = const()[name = string("mh_w_3_transpose_x_0"), val = bool(true)];
            bool mh_w_3_transpose_y_0 = const()[name = string("mh_w_3_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_3_cast_fp16 = matmul(transpose_x = mh_w_3_transpose_x_0, transpose_y = mh_w_3_transpose_y_0, x = var_361_cast_fp16, y = var_363_cast_fp16)[name = string("mh_w_3_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_366_cast_fp16 = softmax(axis = var_304, x = mh_w_3_cast_fp16)[name = string("op_366_cast_fp16")];
            tensor<int32, [4]> var_367 = const()[name = string("op_367"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_368_cast_fp16 = reshape(shape = var_367, x = value_3_cast_fp16)[name = string("op_368_cast_fp16")];
            bool attn_3_transpose_x_0 = const()[name = string("attn_3_transpose_x_0"), val = bool(false)];
            bool attn_3_transpose_y_0 = const()[name = string("attn_3_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_368_cast_fp16, y = var_366_cast_fp16)[name = string("attn_3_cast_fp16")];
            tensor<int32, [4]> var_371 = const()[name = string("op_371"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_9_cast_fp16 = reshape(shape = var_371, x = attn_3_cast_fp16)[name = string("input_9_cast_fp16")];
            string obj_7_pad_type_0 = const()[name = string("obj_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_7_strides_0 = const()[name = string("obj_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_7_pad_0 = const()[name = string("obj_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_7_dilations_0 = const()[name = string("obj_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_7_groups_0 = const()[name = string("obj_7_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63858560)))];
            tensor<fp16, [1280]> layers_1_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67135424)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_7_cast_fp16 = conv(bias = layers_1_self_attn_o_proj_bias_to_fp16, dilations = obj_7_dilations_0, groups = obj_7_groups_0, pad = obj_7_pad_0, pad_type = obj_7_pad_type_0, strides = obj_7_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("obj_7_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = obj_7_cast_fp16)[name = string("inputs_7_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_389_to_fp16 = const()[name = string("op_389_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_389_to_fp16, x = inputs_7_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<fp16, [1280]> input_11_gamma_0_to_fp16 = const()[name = string("input_11_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67138048)))];
            tensor<fp16, [1280]> input_11_beta_0_to_fp16 = const()[name = string("input_11_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67140672)))];
            fp16 input_11_epsilon_0_to_fp16 = const()[name = string("input_11_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_11_cast_fp16 = batch_norm(beta = input_11_beta_0_to_fp16, epsilon = input_11_epsilon_0_to_fp16, gamma = input_11_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_7_cast_fp16)[name = string("input_11_cast_fp16")];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_1_fc1_weight_to_fp16 = const()[name = string("layers_1_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67143296)))];
            tensor<fp16, [5120]> layers_1_fc1_bias_to_fp16 = const()[name = string("layers_1_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80250560)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_13_cast_fp16 = conv(bias = layers_1_fc1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_1_fc1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
            string input_15_mode_0 = const()[name = string("input_15_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_15_cast_fp16 = gelu(mode = input_15_mode_0, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_1_fc2_weight_to_fp16 = const()[name = string("layers_1_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80260864)))];
            tensor<fp16, [1280]> layers_1_fc2_bias_to_fp16 = const()[name = string("layers_1_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93368128)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_7_cast_fp16 = conv(bias = layers_1_fc2_bias_to_fp16, dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_1_fc2_weight_to_fp16, x = input_15_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("inputs_9_cast_fp16")];
            int32 var_422 = const()[name = string("op_422"), val = int32(3)];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_441_to_fp16 = const()[name = string("op_441_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_441_to_fp16, x = inputs_9_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<fp16, [1280]> obj_9_gamma_0_to_fp16 = const()[name = string("obj_9_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93370752)))];
            tensor<fp16, [1280]> obj_9_beta_0_to_fp16 = const()[name = string("obj_9_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93373376)))];
            fp16 obj_9_epsilon_0_to_fp16 = const()[name = string("obj_9_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_9_cast_fp16 = batch_norm(beta = obj_9_beta_0_to_fp16, epsilon = obj_9_epsilon_0_to_fp16, gamma = obj_9_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_9_cast_fp16)[name = string("obj_9_cast_fp16")];
            string query_5_pad_type_0 = const()[name = string("query_5_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_5_strides_0 = const()[name = string("query_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_5_pad_0 = const()[name = string("query_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_5_dilations_0 = const()[name = string("query_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_5_groups_0 = const()[name = string("query_5_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93376000)))];
            tensor<fp16, [1280]> layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96652864)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_5_cast_fp16 = conv(bias = layers_2_self_attn_q_proj_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = obj_9_cast_fp16)[name = string("query_5_cast_fp16")];
            string key_5_pad_type_0 = const()[name = string("key_5_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_5_strides_0 = const()[name = string("key_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_5_pad_0 = const()[name = string("key_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_5_dilations_0 = const()[name = string("key_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_5_groups_0 = const()[name = string("key_5_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96655488)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_5_cast_fp16 = conv(dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = obj_9_cast_fp16)[name = string("key_5_cast_fp16")];
            string value_5_pad_type_0 = const()[name = string("value_5_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_5_strides_0 = const()[name = string("value_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_5_pad_0 = const()[name = string("value_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_5_dilations_0 = const()[name = string("value_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_5_groups_0 = const()[name = string("value_5_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99932352)))];
            tensor<fp16, [1280]> layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103209216)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_5_cast_fp16 = conv(bias = layers_2_self_attn_v_proj_bias_to_fp16, dilations = value_5_dilations_0, groups = value_5_groups_0, pad = value_5_pad_0, pad_type = value_5_pad_type_0, strides = value_5_strides_0, weight = layers_2_self_attn_v_proj_weight_to_fp16, x = obj_9_cast_fp16)[name = string("value_5_cast_fp16")];
            tensor<int32, [4]> var_476 = const()[name = string("op_476"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_5_cast_fp16 = reshape(shape = var_476, x = query_5_cast_fp16)[name = string("mh_q_5_cast_fp16")];
            fp16 var_478_to_fp16 = const()[name = string("op_478_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_479_cast_fp16 = mul(x = mh_q_5_cast_fp16, y = var_478_to_fp16)[name = string("op_479_cast_fp16")];
            tensor<int32, [4]> var_480 = const()[name = string("op_480"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_481_cast_fp16 = reshape(shape = var_480, x = key_5_cast_fp16)[name = string("op_481_cast_fp16")];
            bool mh_w_5_transpose_x_0 = const()[name = string("mh_w_5_transpose_x_0"), val = bool(true)];
            bool mh_w_5_transpose_y_0 = const()[name = string("mh_w_5_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_479_cast_fp16, y = var_481_cast_fp16)[name = string("mh_w_5_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_484_cast_fp16 = softmax(axis = var_422, x = mh_w_5_cast_fp16)[name = string("op_484_cast_fp16")];
            tensor<int32, [4]> var_485 = const()[name = string("op_485"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_486_cast_fp16 = reshape(shape = var_485, x = value_5_cast_fp16)[name = string("op_486_cast_fp16")];
            bool attn_5_transpose_x_0 = const()[name = string("attn_5_transpose_x_0"), val = bool(false)];
            bool attn_5_transpose_y_0 = const()[name = string("attn_5_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_486_cast_fp16, y = var_484_cast_fp16)[name = string("attn_5_cast_fp16")];
            tensor<int32, [4]> var_489 = const()[name = string("op_489"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_17_cast_fp16 = reshape(shape = var_489, x = attn_5_cast_fp16)[name = string("input_17_cast_fp16")];
            string obj_11_pad_type_0 = const()[name = string("obj_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_11_strides_0 = const()[name = string("obj_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_11_pad_0 = const()[name = string("obj_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_11_dilations_0 = const()[name = string("obj_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_11_groups_0 = const()[name = string("obj_11_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103211840)))];
            tensor<fp16, [1280]> layers_2_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106488704)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_11_cast_fp16 = conv(bias = layers_2_self_attn_o_proj_bias_to_fp16, dilations = obj_11_dilations_0, groups = obj_11_groups_0, pad = obj_11_pad_0, pad_type = obj_11_pad_type_0, strides = obj_11_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = input_17_cast_fp16)[name = string("obj_11_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = obj_11_cast_fp16)[name = string("inputs_11_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_507_to_fp16, x = inputs_11_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<fp16, [1280]> input_19_gamma_0_to_fp16 = const()[name = string("input_19_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106491328)))];
            tensor<fp16, [1280]> input_19_beta_0_to_fp16 = const()[name = string("input_19_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106493952)))];
            fp16 input_19_epsilon_0_to_fp16 = const()[name = string("input_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_19_cast_fp16 = batch_norm(beta = input_19_beta_0_to_fp16, epsilon = input_19_epsilon_0_to_fp16, gamma = input_19_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_11_cast_fp16)[name = string("input_19_cast_fp16")];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_2_fc1_weight_to_fp16 = const()[name = string("layers_2_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106496576)))];
            tensor<fp16, [5120]> layers_2_fc1_bias_to_fp16 = const()[name = string("layers_2_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119603840)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_21_cast_fp16 = conv(bias = layers_2_fc1_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_2_fc1_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_23_cast_fp16 = gelu(mode = input_23_mode_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
            string hidden_states_9_pad_type_0 = const()[name = string("hidden_states_9_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_9_strides_0 = const()[name = string("hidden_states_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = string("hidden_states_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_9_dilations_0 = const()[name = string("hidden_states_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_9_groups_0 = const()[name = string("hidden_states_9_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_2_fc2_weight_to_fp16 = const()[name = string("layers_2_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119614144)))];
            tensor<fp16, [1280]> layers_2_fc2_bias_to_fp16 = const()[name = string("layers_2_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132721408)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_9_cast_fp16 = conv(bias = layers_2_fc2_bias_to_fp16, dilations = hidden_states_9_dilations_0, groups = hidden_states_9_groups_0, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = hidden_states_9_strides_0, weight = layers_2_fc2_weight_to_fp16, x = input_23_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = hidden_states_9_cast_fp16)[name = string("inputs_13_cast_fp16")];
            int32 var_540 = const()[name = string("op_540"), val = int32(3)];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_559_to_fp16 = const()[name = string("op_559_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_559_to_fp16, x = inputs_13_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<fp16, [1280]> obj_13_gamma_0_to_fp16 = const()[name = string("obj_13_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132724032)))];
            tensor<fp16, [1280]> obj_13_beta_0_to_fp16 = const()[name = string("obj_13_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132726656)))];
            fp16 obj_13_epsilon_0_to_fp16 = const()[name = string("obj_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_13_cast_fp16 = batch_norm(beta = obj_13_beta_0_to_fp16, epsilon = obj_13_epsilon_0_to_fp16, gamma = obj_13_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_13_cast_fp16)[name = string("obj_13_cast_fp16")];
            string query_7_pad_type_0 = const()[name = string("query_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_7_strides_0 = const()[name = string("query_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_7_pad_0 = const()[name = string("query_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_7_dilations_0 = const()[name = string("query_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_7_groups_0 = const()[name = string("query_7_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132729280)))];
            tensor<fp16, [1280]> layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136006144)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_7_cast_fp16 = conv(bias = layers_3_self_attn_q_proj_bias_to_fp16, dilations = query_7_dilations_0, groups = query_7_groups_0, pad = query_7_pad_0, pad_type = query_7_pad_type_0, strides = query_7_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = obj_13_cast_fp16)[name = string("query_7_cast_fp16")];
            string key_7_pad_type_0 = const()[name = string("key_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_7_strides_0 = const()[name = string("key_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_7_pad_0 = const()[name = string("key_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_7_dilations_0 = const()[name = string("key_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_7_groups_0 = const()[name = string("key_7_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136008768)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_7_cast_fp16 = conv(dilations = key_7_dilations_0, groups = key_7_groups_0, pad = key_7_pad_0, pad_type = key_7_pad_type_0, strides = key_7_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = obj_13_cast_fp16)[name = string("key_7_cast_fp16")];
            string value_7_pad_type_0 = const()[name = string("value_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_7_strides_0 = const()[name = string("value_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_7_pad_0 = const()[name = string("value_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_7_dilations_0 = const()[name = string("value_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_7_groups_0 = const()[name = string("value_7_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139285632)))];
            tensor<fp16, [1280]> layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142562496)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_7_cast_fp16 = conv(bias = layers_3_self_attn_v_proj_bias_to_fp16, dilations = value_7_dilations_0, groups = value_7_groups_0, pad = value_7_pad_0, pad_type = value_7_pad_type_0, strides = value_7_strides_0, weight = layers_3_self_attn_v_proj_weight_to_fp16, x = obj_13_cast_fp16)[name = string("value_7_cast_fp16")];
            tensor<int32, [4]> var_594 = const()[name = string("op_594"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_7_cast_fp16 = reshape(shape = var_594, x = query_7_cast_fp16)[name = string("mh_q_7_cast_fp16")];
            fp16 var_596_to_fp16 = const()[name = string("op_596_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_597_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = var_596_to_fp16)[name = string("op_597_cast_fp16")];
            tensor<int32, [4]> var_598 = const()[name = string("op_598"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_599_cast_fp16 = reshape(shape = var_598, x = key_7_cast_fp16)[name = string("op_599_cast_fp16")];
            bool mh_w_7_transpose_x_0 = const()[name = string("mh_w_7_transpose_x_0"), val = bool(true)];
            bool mh_w_7_transpose_y_0 = const()[name = string("mh_w_7_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_7_cast_fp16 = matmul(transpose_x = mh_w_7_transpose_x_0, transpose_y = mh_w_7_transpose_y_0, x = var_597_cast_fp16, y = var_599_cast_fp16)[name = string("mh_w_7_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_602_cast_fp16 = softmax(axis = var_540, x = mh_w_7_cast_fp16)[name = string("op_602_cast_fp16")];
            tensor<int32, [4]> var_603 = const()[name = string("op_603"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_604_cast_fp16 = reshape(shape = var_603, x = value_7_cast_fp16)[name = string("op_604_cast_fp16")];
            bool attn_7_transpose_x_0 = const()[name = string("attn_7_transpose_x_0"), val = bool(false)];
            bool attn_7_transpose_y_0 = const()[name = string("attn_7_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_604_cast_fp16, y = var_602_cast_fp16)[name = string("attn_7_cast_fp16")];
            tensor<int32, [4]> var_607 = const()[name = string("op_607"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_25_cast_fp16 = reshape(shape = var_607, x = attn_7_cast_fp16)[name = string("input_25_cast_fp16")];
            string obj_15_pad_type_0 = const()[name = string("obj_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_15_strides_0 = const()[name = string("obj_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_15_pad_0 = const()[name = string("obj_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_15_dilations_0 = const()[name = string("obj_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_15_groups_0 = const()[name = string("obj_15_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142565120)))];
            tensor<fp16, [1280]> layers_3_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_3_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145841984)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_15_cast_fp16 = conv(bias = layers_3_self_attn_o_proj_bias_to_fp16, dilations = obj_15_dilations_0, groups = obj_15_groups_0, pad = obj_15_pad_0, pad_type = obj_15_pad_type_0, strides = obj_15_strides_0, weight = layers_3_self_attn_o_proj_weight_to_fp16, x = input_25_cast_fp16)[name = string("obj_15_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = obj_15_cast_fp16)[name = string("inputs_15_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_625_to_fp16, x = inputs_15_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<fp16, [1280]> input_27_gamma_0_to_fp16 = const()[name = string("input_27_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145844608)))];
            tensor<fp16, [1280]> input_27_beta_0_to_fp16 = const()[name = string("input_27_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145847232)))];
            fp16 input_27_epsilon_0_to_fp16 = const()[name = string("input_27_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_27_cast_fp16 = batch_norm(beta = input_27_beta_0_to_fp16, epsilon = input_27_epsilon_0_to_fp16, gamma = input_27_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_15_cast_fp16)[name = string("input_27_cast_fp16")];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_3_fc1_weight_to_fp16 = const()[name = string("layers_3_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145849856)))];
            tensor<fp16, [5120]> layers_3_fc1_bias_to_fp16 = const()[name = string("layers_3_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158957120)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_29_cast_fp16 = conv(bias = layers_3_fc1_bias_to_fp16, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_3_fc1_weight_to_fp16, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
            string input_31_mode_0 = const()[name = string("input_31_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_31_cast_fp16 = gelu(mode = input_31_mode_0, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
            string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_3_fc2_weight_to_fp16 = const()[name = string("layers_3_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158967424)))];
            tensor<fp16, [1280]> layers_3_fc2_bias_to_fp16 = const()[name = string("layers_3_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172074688)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_11_cast_fp16 = conv(bias = layers_3_fc2_bias_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = layers_3_fc2_weight_to_fp16, x = input_31_cast_fp16)[name = string("hidden_states_11_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = hidden_states_11_cast_fp16)[name = string("inputs_17_cast_fp16")];
            int32 var_658 = const()[name = string("op_658"), val = int32(3)];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_677_to_fp16, x = inputs_17_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<fp16, [1280]> obj_17_gamma_0_to_fp16 = const()[name = string("obj_17_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172077312)))];
            tensor<fp16, [1280]> obj_17_beta_0_to_fp16 = const()[name = string("obj_17_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172079936)))];
            fp16 obj_17_epsilon_0_to_fp16 = const()[name = string("obj_17_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_17_cast_fp16 = batch_norm(beta = obj_17_beta_0_to_fp16, epsilon = obj_17_epsilon_0_to_fp16, gamma = obj_17_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_17_cast_fp16)[name = string("obj_17_cast_fp16")];
            string query_9_pad_type_0 = const()[name = string("query_9_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_9_strides_0 = const()[name = string("query_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_9_pad_0 = const()[name = string("query_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_9_dilations_0 = const()[name = string("query_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_9_groups_0 = const()[name = string("query_9_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172082560)))];
            tensor<fp16, [1280]> layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175359424)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_9_cast_fp16 = conv(bias = layers_4_self_attn_q_proj_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = obj_17_cast_fp16)[name = string("query_9_cast_fp16")];
            string key_9_pad_type_0 = const()[name = string("key_9_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_9_strides_0 = const()[name = string("key_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_9_pad_0 = const()[name = string("key_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_9_dilations_0 = const()[name = string("key_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_9_groups_0 = const()[name = string("key_9_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175362048)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_9_cast_fp16 = conv(dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = obj_17_cast_fp16)[name = string("key_9_cast_fp16")];
            string value_9_pad_type_0 = const()[name = string("value_9_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_9_strides_0 = const()[name = string("value_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_9_pad_0 = const()[name = string("value_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_9_dilations_0 = const()[name = string("value_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_9_groups_0 = const()[name = string("value_9_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178638912)))];
            tensor<fp16, [1280]> layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181915776)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_9_cast_fp16 = conv(bias = layers_4_self_attn_v_proj_bias_to_fp16, dilations = value_9_dilations_0, groups = value_9_groups_0, pad = value_9_pad_0, pad_type = value_9_pad_type_0, strides = value_9_strides_0, weight = layers_4_self_attn_v_proj_weight_to_fp16, x = obj_17_cast_fp16)[name = string("value_9_cast_fp16")];
            tensor<int32, [4]> var_712 = const()[name = string("op_712"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_9_cast_fp16 = reshape(shape = var_712, x = query_9_cast_fp16)[name = string("mh_q_9_cast_fp16")];
            fp16 var_714_to_fp16 = const()[name = string("op_714_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_715_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_714_to_fp16)[name = string("op_715_cast_fp16")];
            tensor<int32, [4]> var_716 = const()[name = string("op_716"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_717_cast_fp16 = reshape(shape = var_716, x = key_9_cast_fp16)[name = string("op_717_cast_fp16")];
            bool mh_w_9_transpose_x_0 = const()[name = string("mh_w_9_transpose_x_0"), val = bool(true)];
            bool mh_w_9_transpose_y_0 = const()[name = string("mh_w_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_9_cast_fp16 = matmul(transpose_x = mh_w_9_transpose_x_0, transpose_y = mh_w_9_transpose_y_0, x = var_715_cast_fp16, y = var_717_cast_fp16)[name = string("mh_w_9_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_720_cast_fp16 = softmax(axis = var_658, x = mh_w_9_cast_fp16)[name = string("op_720_cast_fp16")];
            tensor<int32, [4]> var_721 = const()[name = string("op_721"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_722_cast_fp16 = reshape(shape = var_721, x = value_9_cast_fp16)[name = string("op_722_cast_fp16")];
            bool attn_9_transpose_x_0 = const()[name = string("attn_9_transpose_x_0"), val = bool(false)];
            bool attn_9_transpose_y_0 = const()[name = string("attn_9_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_722_cast_fp16, y = var_720_cast_fp16)[name = string("attn_9_cast_fp16")];
            tensor<int32, [4]> var_725 = const()[name = string("op_725"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_33_cast_fp16 = reshape(shape = var_725, x = attn_9_cast_fp16)[name = string("input_33_cast_fp16")];
            string obj_19_pad_type_0 = const()[name = string("obj_19_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_19_strides_0 = const()[name = string("obj_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_19_pad_0 = const()[name = string("obj_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_19_dilations_0 = const()[name = string("obj_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_19_groups_0 = const()[name = string("obj_19_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181918400)))];
            tensor<fp16, [1280]> layers_4_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_4_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185195264)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_19_cast_fp16 = conv(bias = layers_4_self_attn_o_proj_bias_to_fp16, dilations = obj_19_dilations_0, groups = obj_19_groups_0, pad = obj_19_pad_0, pad_type = obj_19_pad_type_0, strides = obj_19_strides_0, weight = layers_4_self_attn_o_proj_weight_to_fp16, x = input_33_cast_fp16)[name = string("obj_19_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = obj_19_cast_fp16)[name = string("inputs_19_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_743_to_fp16 = const()[name = string("op_743_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_743_to_fp16, x = inputs_19_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<fp16, [1280]> input_35_gamma_0_to_fp16 = const()[name = string("input_35_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185197888)))];
            tensor<fp16, [1280]> input_35_beta_0_to_fp16 = const()[name = string("input_35_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185200512)))];
            fp16 input_35_epsilon_0_to_fp16 = const()[name = string("input_35_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_35_cast_fp16 = batch_norm(beta = input_35_beta_0_to_fp16, epsilon = input_35_epsilon_0_to_fp16, gamma = input_35_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_19_cast_fp16)[name = string("input_35_cast_fp16")];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_4_fc1_weight_to_fp16 = const()[name = string("layers_4_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185203136)))];
            tensor<fp16, [5120]> layers_4_fc1_bias_to_fp16 = const()[name = string("layers_4_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198310400)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_37_cast_fp16 = conv(bias = layers_4_fc1_bias_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_4_fc1_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
            string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_39_cast_fp16 = gelu(mode = input_39_mode_0, x = input_37_cast_fp16)[name = string("input_39_cast_fp16")];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_4_fc2_weight_to_fp16 = const()[name = string("layers_4_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198320704)))];
            tensor<fp16, [1280]> layers_4_fc2_bias_to_fp16 = const()[name = string("layers_4_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211427968)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_13_cast_fp16 = conv(bias = layers_4_fc2_bias_to_fp16, dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_4_fc2_weight_to_fp16, x = input_39_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("inputs_21_cast_fp16")];
            int32 var_776 = const()[name = string("op_776"), val = int32(3)];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_795_to_fp16 = const()[name = string("op_795_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_795_to_fp16, x = inputs_21_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<fp16, [1280]> obj_21_gamma_0_to_fp16 = const()[name = string("obj_21_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211430592)))];
            tensor<fp16, [1280]> obj_21_beta_0_to_fp16 = const()[name = string("obj_21_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211433216)))];
            fp16 obj_21_epsilon_0_to_fp16 = const()[name = string("obj_21_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_21_cast_fp16 = batch_norm(beta = obj_21_beta_0_to_fp16, epsilon = obj_21_epsilon_0_to_fp16, gamma = obj_21_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_21_cast_fp16)[name = string("obj_21_cast_fp16")];
            string query_11_pad_type_0 = const()[name = string("query_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_11_strides_0 = const()[name = string("query_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_11_pad_0 = const()[name = string("query_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_11_dilations_0 = const()[name = string("query_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_11_groups_0 = const()[name = string("query_11_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211435840)))];
            tensor<fp16, [1280]> layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214712704)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_11_cast_fp16 = conv(bias = layers_5_self_attn_q_proj_bias_to_fp16, dilations = query_11_dilations_0, groups = query_11_groups_0, pad = query_11_pad_0, pad_type = query_11_pad_type_0, strides = query_11_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = obj_21_cast_fp16)[name = string("query_11_cast_fp16")];
            string key_11_pad_type_0 = const()[name = string("key_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_11_strides_0 = const()[name = string("key_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_11_pad_0 = const()[name = string("key_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_11_dilations_0 = const()[name = string("key_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_11_groups_0 = const()[name = string("key_11_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214715328)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_11_cast_fp16 = conv(dilations = key_11_dilations_0, groups = key_11_groups_0, pad = key_11_pad_0, pad_type = key_11_pad_type_0, strides = key_11_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = obj_21_cast_fp16)[name = string("key_11_cast_fp16")];
            string value_11_pad_type_0 = const()[name = string("value_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_11_strides_0 = const()[name = string("value_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_11_pad_0 = const()[name = string("value_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_11_dilations_0 = const()[name = string("value_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_11_groups_0 = const()[name = string("value_11_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217992192)))];
            tensor<fp16, [1280]> layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221269056)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_11_cast_fp16 = conv(bias = layers_5_self_attn_v_proj_bias_to_fp16, dilations = value_11_dilations_0, groups = value_11_groups_0, pad = value_11_pad_0, pad_type = value_11_pad_type_0, strides = value_11_strides_0, weight = layers_5_self_attn_v_proj_weight_to_fp16, x = obj_21_cast_fp16)[name = string("value_11_cast_fp16")];
            tensor<int32, [4]> var_830 = const()[name = string("op_830"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_11_cast_fp16 = reshape(shape = var_830, x = query_11_cast_fp16)[name = string("mh_q_11_cast_fp16")];
            fp16 var_832_to_fp16 = const()[name = string("op_832_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_833_cast_fp16 = mul(x = mh_q_11_cast_fp16, y = var_832_to_fp16)[name = string("op_833_cast_fp16")];
            tensor<int32, [4]> var_834 = const()[name = string("op_834"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_835_cast_fp16 = reshape(shape = var_834, x = key_11_cast_fp16)[name = string("op_835_cast_fp16")];
            bool mh_w_11_transpose_x_0 = const()[name = string("mh_w_11_transpose_x_0"), val = bool(true)];
            bool mh_w_11_transpose_y_0 = const()[name = string("mh_w_11_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_11_cast_fp16 = matmul(transpose_x = mh_w_11_transpose_x_0, transpose_y = mh_w_11_transpose_y_0, x = var_833_cast_fp16, y = var_835_cast_fp16)[name = string("mh_w_11_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_838_cast_fp16 = softmax(axis = var_776, x = mh_w_11_cast_fp16)[name = string("op_838_cast_fp16")];
            tensor<int32, [4]> var_839 = const()[name = string("op_839"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_840_cast_fp16 = reshape(shape = var_839, x = value_11_cast_fp16)[name = string("op_840_cast_fp16")];
            bool attn_11_transpose_x_0 = const()[name = string("attn_11_transpose_x_0"), val = bool(false)];
            bool attn_11_transpose_y_0 = const()[name = string("attn_11_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_840_cast_fp16, y = var_838_cast_fp16)[name = string("attn_11_cast_fp16")];
            tensor<int32, [4]> var_843 = const()[name = string("op_843"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_41_cast_fp16 = reshape(shape = var_843, x = attn_11_cast_fp16)[name = string("input_41_cast_fp16")];
            string obj_23_pad_type_0 = const()[name = string("obj_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_23_strides_0 = const()[name = string("obj_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_23_pad_0 = const()[name = string("obj_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_23_dilations_0 = const()[name = string("obj_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_23_groups_0 = const()[name = string("obj_23_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221271680)))];
            tensor<fp16, [1280]> layers_5_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_5_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224548544)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_23_cast_fp16 = conv(bias = layers_5_self_attn_o_proj_bias_to_fp16, dilations = obj_23_dilations_0, groups = obj_23_groups_0, pad = obj_23_pad_0, pad_type = obj_23_pad_type_0, strides = obj_23_strides_0, weight = layers_5_self_attn_o_proj_weight_to_fp16, x = input_41_cast_fp16)[name = string("obj_23_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = obj_23_cast_fp16)[name = string("inputs_23_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_861_to_fp16 = const()[name = string("op_861_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_861_to_fp16, x = inputs_23_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<fp16, [1280]> input_43_gamma_0_to_fp16 = const()[name = string("input_43_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224551168)))];
            tensor<fp16, [1280]> input_43_beta_0_to_fp16 = const()[name = string("input_43_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224553792)))];
            fp16 input_43_epsilon_0_to_fp16 = const()[name = string("input_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_43_cast_fp16 = batch_norm(beta = input_43_beta_0_to_fp16, epsilon = input_43_epsilon_0_to_fp16, gamma = input_43_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_23_cast_fp16)[name = string("input_43_cast_fp16")];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_5_fc1_weight_to_fp16 = const()[name = string("layers_5_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224556416)))];
            tensor<fp16, [5120]> layers_5_fc1_bias_to_fp16 = const()[name = string("layers_5_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237663680)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_45_cast_fp16 = conv(bias = layers_5_fc1_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_5_fc1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_47_cast_fp16 = gelu(mode = input_47_mode_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
            string hidden_states_15_pad_type_0 = const()[name = string("hidden_states_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_15_strides_0 = const()[name = string("hidden_states_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_15_pad_0 = const()[name = string("hidden_states_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_15_dilations_0 = const()[name = string("hidden_states_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_15_groups_0 = const()[name = string("hidden_states_15_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_5_fc2_weight_to_fp16 = const()[name = string("layers_5_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237673984)))];
            tensor<fp16, [1280]> layers_5_fc2_bias_to_fp16 = const()[name = string("layers_5_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250781248)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_15_cast_fp16 = conv(bias = layers_5_fc2_bias_to_fp16, dilations = hidden_states_15_dilations_0, groups = hidden_states_15_groups_0, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = hidden_states_15_strides_0, weight = layers_5_fc2_weight_to_fp16, x = input_47_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = hidden_states_15_cast_fp16)[name = string("inputs_25_cast_fp16")];
            int32 var_894 = const()[name = string("op_894"), val = int32(3)];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_913_to_fp16 = const()[name = string("op_913_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_913_to_fp16, x = inputs_25_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<fp16, [1280]> obj_25_gamma_0_to_fp16 = const()[name = string("obj_25_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250783872)))];
            tensor<fp16, [1280]> obj_25_beta_0_to_fp16 = const()[name = string("obj_25_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250786496)))];
            fp16 obj_25_epsilon_0_to_fp16 = const()[name = string("obj_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_25_cast_fp16 = batch_norm(beta = obj_25_beta_0_to_fp16, epsilon = obj_25_epsilon_0_to_fp16, gamma = obj_25_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_25_cast_fp16)[name = string("obj_25_cast_fp16")];
            string query_13_pad_type_0 = const()[name = string("query_13_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_13_strides_0 = const()[name = string("query_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_13_pad_0 = const()[name = string("query_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_13_dilations_0 = const()[name = string("query_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_13_groups_0 = const()[name = string("query_13_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250789120)))];
            tensor<fp16, [1280]> layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254065984)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_13_cast_fp16 = conv(bias = layers_6_self_attn_q_proj_bias_to_fp16, dilations = query_13_dilations_0, groups = query_13_groups_0, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = query_13_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = obj_25_cast_fp16)[name = string("query_13_cast_fp16")];
            string key_13_pad_type_0 = const()[name = string("key_13_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_13_strides_0 = const()[name = string("key_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_13_pad_0 = const()[name = string("key_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_13_dilations_0 = const()[name = string("key_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_13_groups_0 = const()[name = string("key_13_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254068608)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_13_cast_fp16 = conv(dilations = key_13_dilations_0, groups = key_13_groups_0, pad = key_13_pad_0, pad_type = key_13_pad_type_0, strides = key_13_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = obj_25_cast_fp16)[name = string("key_13_cast_fp16")];
            string value_13_pad_type_0 = const()[name = string("value_13_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_13_strides_0 = const()[name = string("value_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_13_pad_0 = const()[name = string("value_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_13_dilations_0 = const()[name = string("value_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_13_groups_0 = const()[name = string("value_13_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257345472)))];
            tensor<fp16, [1280]> layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260622336)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_13_cast_fp16 = conv(bias = layers_6_self_attn_v_proj_bias_to_fp16, dilations = value_13_dilations_0, groups = value_13_groups_0, pad = value_13_pad_0, pad_type = value_13_pad_type_0, strides = value_13_strides_0, weight = layers_6_self_attn_v_proj_weight_to_fp16, x = obj_25_cast_fp16)[name = string("value_13_cast_fp16")];
            tensor<int32, [4]> var_948 = const()[name = string("op_948"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_13_cast_fp16 = reshape(shape = var_948, x = query_13_cast_fp16)[name = string("mh_q_13_cast_fp16")];
            fp16 var_950_to_fp16 = const()[name = string("op_950_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_951_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = var_950_to_fp16)[name = string("op_951_cast_fp16")];
            tensor<int32, [4]> var_952 = const()[name = string("op_952"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_953_cast_fp16 = reshape(shape = var_952, x = key_13_cast_fp16)[name = string("op_953_cast_fp16")];
            bool mh_w_13_transpose_x_0 = const()[name = string("mh_w_13_transpose_x_0"), val = bool(true)];
            bool mh_w_13_transpose_y_0 = const()[name = string("mh_w_13_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_951_cast_fp16, y = var_953_cast_fp16)[name = string("mh_w_13_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_956_cast_fp16 = softmax(axis = var_894, x = mh_w_13_cast_fp16)[name = string("op_956_cast_fp16")];
            tensor<int32, [4]> var_957 = const()[name = string("op_957"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_958_cast_fp16 = reshape(shape = var_957, x = value_13_cast_fp16)[name = string("op_958_cast_fp16")];
            bool attn_13_transpose_x_0 = const()[name = string("attn_13_transpose_x_0"), val = bool(false)];
            bool attn_13_transpose_y_0 = const()[name = string("attn_13_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_958_cast_fp16, y = var_956_cast_fp16)[name = string("attn_13_cast_fp16")];
            tensor<int32, [4]> var_961 = const()[name = string("op_961"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_49_cast_fp16 = reshape(shape = var_961, x = attn_13_cast_fp16)[name = string("input_49_cast_fp16")];
            string obj_27_pad_type_0 = const()[name = string("obj_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_27_strides_0 = const()[name = string("obj_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_27_pad_0 = const()[name = string("obj_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_27_dilations_0 = const()[name = string("obj_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_27_groups_0 = const()[name = string("obj_27_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260624960)))];
            tensor<fp16, [1280]> layers_6_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_6_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263901824)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_27_cast_fp16 = conv(bias = layers_6_self_attn_o_proj_bias_to_fp16, dilations = obj_27_dilations_0, groups = obj_27_groups_0, pad = obj_27_pad_0, pad_type = obj_27_pad_type_0, strides = obj_27_strides_0, weight = layers_6_self_attn_o_proj_weight_to_fp16, x = input_49_cast_fp16)[name = string("obj_27_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = obj_27_cast_fp16)[name = string("inputs_27_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_979_to_fp16 = const()[name = string("op_979_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_979_to_fp16, x = inputs_27_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<fp16, [1280]> input_51_gamma_0_to_fp16 = const()[name = string("input_51_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263904448)))];
            tensor<fp16, [1280]> input_51_beta_0_to_fp16 = const()[name = string("input_51_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263907072)))];
            fp16 input_51_epsilon_0_to_fp16 = const()[name = string("input_51_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_51_cast_fp16 = batch_norm(beta = input_51_beta_0_to_fp16, epsilon = input_51_epsilon_0_to_fp16, gamma = input_51_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_27_cast_fp16)[name = string("input_51_cast_fp16")];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_6_fc1_weight_to_fp16 = const()[name = string("layers_6_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263909696)))];
            tensor<fp16, [5120]> layers_6_fc1_bias_to_fp16 = const()[name = string("layers_6_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277016960)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_53_cast_fp16 = conv(bias = layers_6_fc1_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_6_fc1_weight_to_fp16, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_6_fc2_weight_to_fp16 = const()[name = string("layers_6_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277027264)))];
            tensor<fp16, [1280]> layers_6_fc2_bias_to_fp16 = const()[name = string("layers_6_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290134528)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_17_cast_fp16 = conv(bias = layers_6_fc2_bias_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_6_fc2_weight_to_fp16, x = input_55_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("inputs_29_cast_fp16")];
            int32 var_1012 = const()[name = string("op_1012"), val = int32(3)];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1031_to_fp16 = const()[name = string("op_1031_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_1031_to_fp16, x = inputs_29_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<fp16, [1280]> obj_29_gamma_0_to_fp16 = const()[name = string("obj_29_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290137152)))];
            tensor<fp16, [1280]> obj_29_beta_0_to_fp16 = const()[name = string("obj_29_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290139776)))];
            fp16 obj_29_epsilon_0_to_fp16 = const()[name = string("obj_29_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_29_cast_fp16 = batch_norm(beta = obj_29_beta_0_to_fp16, epsilon = obj_29_epsilon_0_to_fp16, gamma = obj_29_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_29_cast_fp16)[name = string("obj_29_cast_fp16")];
            string query_15_pad_type_0 = const()[name = string("query_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_15_strides_0 = const()[name = string("query_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_15_pad_0 = const()[name = string("query_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_15_dilations_0 = const()[name = string("query_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_15_groups_0 = const()[name = string("query_15_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290142400)))];
            tensor<fp16, [1280]> layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293419264)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_15_cast_fp16 = conv(bias = layers_7_self_attn_q_proj_bias_to_fp16, dilations = query_15_dilations_0, groups = query_15_groups_0, pad = query_15_pad_0, pad_type = query_15_pad_type_0, strides = query_15_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = string("query_15_cast_fp16")];
            string key_15_pad_type_0 = const()[name = string("key_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_15_strides_0 = const()[name = string("key_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_15_pad_0 = const()[name = string("key_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_15_dilations_0 = const()[name = string("key_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_15_groups_0 = const()[name = string("key_15_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293421888)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_15_cast_fp16 = conv(dilations = key_15_dilations_0, groups = key_15_groups_0, pad = key_15_pad_0, pad_type = key_15_pad_type_0, strides = key_15_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = string("key_15_cast_fp16")];
            string value_15_pad_type_0 = const()[name = string("value_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_15_strides_0 = const()[name = string("value_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_15_pad_0 = const()[name = string("value_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_15_dilations_0 = const()[name = string("value_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_15_groups_0 = const()[name = string("value_15_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296698752)))];
            tensor<fp16, [1280]> layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299975616)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_15_cast_fp16 = conv(bias = layers_7_self_attn_v_proj_bias_to_fp16, dilations = value_15_dilations_0, groups = value_15_groups_0, pad = value_15_pad_0, pad_type = value_15_pad_type_0, strides = value_15_strides_0, weight = layers_7_self_attn_v_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = string("value_15_cast_fp16")];
            tensor<int32, [4]> var_1066 = const()[name = string("op_1066"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_15_cast_fp16 = reshape(shape = var_1066, x = query_15_cast_fp16)[name = string("mh_q_15_cast_fp16")];
            fp16 var_1068_to_fp16 = const()[name = string("op_1068_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1069_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_1068_to_fp16)[name = string("op_1069_cast_fp16")];
            tensor<int32, [4]> var_1070 = const()[name = string("op_1070"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1071_cast_fp16 = reshape(shape = var_1070, x = key_15_cast_fp16)[name = string("op_1071_cast_fp16")];
            bool mh_w_15_transpose_x_0 = const()[name = string("mh_w_15_transpose_x_0"), val = bool(true)];
            bool mh_w_15_transpose_y_0 = const()[name = string("mh_w_15_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_15_cast_fp16 = matmul(transpose_x = mh_w_15_transpose_x_0, transpose_y = mh_w_15_transpose_y_0, x = var_1069_cast_fp16, y = var_1071_cast_fp16)[name = string("mh_w_15_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1074_cast_fp16 = softmax(axis = var_1012, x = mh_w_15_cast_fp16)[name = string("op_1074_cast_fp16")];
            tensor<int32, [4]> var_1075 = const()[name = string("op_1075"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1076_cast_fp16 = reshape(shape = var_1075, x = value_15_cast_fp16)[name = string("op_1076_cast_fp16")];
            bool attn_15_transpose_x_0 = const()[name = string("attn_15_transpose_x_0"), val = bool(false)];
            bool attn_15_transpose_y_0 = const()[name = string("attn_15_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1076_cast_fp16, y = var_1074_cast_fp16)[name = string("attn_15_cast_fp16")];
            tensor<int32, [4]> var_1079 = const()[name = string("op_1079"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_57_cast_fp16 = reshape(shape = var_1079, x = attn_15_cast_fp16)[name = string("input_57_cast_fp16")];
            string obj_31_pad_type_0 = const()[name = string("obj_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_31_strides_0 = const()[name = string("obj_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_31_pad_0 = const()[name = string("obj_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_31_dilations_0 = const()[name = string("obj_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_31_groups_0 = const()[name = string("obj_31_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299978240)))];
            tensor<fp16, [1280]> layers_7_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_7_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303255104)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_31_cast_fp16 = conv(bias = layers_7_self_attn_o_proj_bias_to_fp16, dilations = obj_31_dilations_0, groups = obj_31_groups_0, pad = obj_31_pad_0, pad_type = obj_31_pad_type_0, strides = obj_31_strides_0, weight = layers_7_self_attn_o_proj_weight_to_fp16, x = input_57_cast_fp16)[name = string("obj_31_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = obj_31_cast_fp16)[name = string("inputs_31_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1097_to_fp16 = const()[name = string("op_1097_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_1097_to_fp16, x = inputs_31_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<fp16, [1280]> input_59_gamma_0_to_fp16 = const()[name = string("input_59_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303257728)))];
            tensor<fp16, [1280]> input_59_beta_0_to_fp16 = const()[name = string("input_59_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303260352)))];
            fp16 input_59_epsilon_0_to_fp16 = const()[name = string("input_59_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_59_cast_fp16 = batch_norm(beta = input_59_beta_0_to_fp16, epsilon = input_59_epsilon_0_to_fp16, gamma = input_59_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_31_cast_fp16)[name = string("input_59_cast_fp16")];
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_7_fc1_weight_to_fp16 = const()[name = string("layers_7_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303262976)))];
            tensor<fp16, [5120]> layers_7_fc1_bias_to_fp16 = const()[name = string("layers_7_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316370240)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_61_cast_fp16 = conv(bias = layers_7_fc1_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = layers_7_fc1_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            string input_63_mode_0 = const()[name = string("input_63_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_63_cast_fp16 = gelu(mode = input_63_mode_0, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
            string hidden_states_19_pad_type_0 = const()[name = string("hidden_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = string("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = string("hidden_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_19_dilations_0 = const()[name = string("hidden_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_19_groups_0 = const()[name = string("hidden_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_7_fc2_weight_to_fp16 = const()[name = string("layers_7_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316380544)))];
            tensor<fp16, [1280]> layers_7_fc2_bias_to_fp16 = const()[name = string("layers_7_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329487808)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_19_cast_fp16 = conv(bias = layers_7_fc2_bias_to_fp16, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = layers_7_fc2_weight_to_fp16, x = input_63_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_33_cast_fp16 = add(x = inputs_31_cast_fp16, y = hidden_states_19_cast_fp16)[name = string("inputs_33_cast_fp16")];
            int32 var_1130 = const()[name = string("op_1130"), val = int32(3)];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1149_to_fp16 = const()[name = string("op_1149_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_1149_to_fp16, x = inputs_33_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<fp16, [1280]> obj_33_gamma_0_to_fp16 = const()[name = string("obj_33_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329490432)))];
            tensor<fp16, [1280]> obj_33_beta_0_to_fp16 = const()[name = string("obj_33_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329493056)))];
            fp16 obj_33_epsilon_0_to_fp16 = const()[name = string("obj_33_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_33_cast_fp16 = batch_norm(beta = obj_33_beta_0_to_fp16, epsilon = obj_33_epsilon_0_to_fp16, gamma = obj_33_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_33_cast_fp16)[name = string("obj_33_cast_fp16")];
            string query_17_pad_type_0 = const()[name = string("query_17_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_17_strides_0 = const()[name = string("query_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_17_pad_0 = const()[name = string("query_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_17_dilations_0 = const()[name = string("query_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_17_groups_0 = const()[name = string("query_17_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329495680)))];
            tensor<fp16, [1280]> layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332772544)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_17_cast_fp16 = conv(bias = layers_8_self_attn_q_proj_bias_to_fp16, dilations = query_17_dilations_0, groups = query_17_groups_0, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = query_17_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = obj_33_cast_fp16)[name = string("query_17_cast_fp16")];
            string key_17_pad_type_0 = const()[name = string("key_17_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_17_strides_0 = const()[name = string("key_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_17_pad_0 = const()[name = string("key_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_17_dilations_0 = const()[name = string("key_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_17_groups_0 = const()[name = string("key_17_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332775168)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_17_cast_fp16 = conv(dilations = key_17_dilations_0, groups = key_17_groups_0, pad = key_17_pad_0, pad_type = key_17_pad_type_0, strides = key_17_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = obj_33_cast_fp16)[name = string("key_17_cast_fp16")];
            string value_17_pad_type_0 = const()[name = string("value_17_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_17_strides_0 = const()[name = string("value_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_17_pad_0 = const()[name = string("value_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_17_dilations_0 = const()[name = string("value_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_17_groups_0 = const()[name = string("value_17_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336052032)))];
            tensor<fp16, [1280]> layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339328896)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_17_cast_fp16 = conv(bias = layers_8_self_attn_v_proj_bias_to_fp16, dilations = value_17_dilations_0, groups = value_17_groups_0, pad = value_17_pad_0, pad_type = value_17_pad_type_0, strides = value_17_strides_0, weight = layers_8_self_attn_v_proj_weight_to_fp16, x = obj_33_cast_fp16)[name = string("value_17_cast_fp16")];
            tensor<int32, [4]> var_1184 = const()[name = string("op_1184"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_17_cast_fp16 = reshape(shape = var_1184, x = query_17_cast_fp16)[name = string("mh_q_17_cast_fp16")];
            fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1187_cast_fp16 = mul(x = mh_q_17_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")];
            tensor<int32, [4]> var_1188 = const()[name = string("op_1188"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1189_cast_fp16 = reshape(shape = var_1188, x = key_17_cast_fp16)[name = string("op_1189_cast_fp16")];
            bool mh_w_17_transpose_x_0 = const()[name = string("mh_w_17_transpose_x_0"), val = bool(true)];
            bool mh_w_17_transpose_y_0 = const()[name = string("mh_w_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_1187_cast_fp16, y = var_1189_cast_fp16)[name = string("mh_w_17_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1192_cast_fp16 = softmax(axis = var_1130, x = mh_w_17_cast_fp16)[name = string("op_1192_cast_fp16")];
            tensor<int32, [4]> var_1193 = const()[name = string("op_1193"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1194_cast_fp16 = reshape(shape = var_1193, x = value_17_cast_fp16)[name = string("op_1194_cast_fp16")];
            bool attn_17_transpose_x_0 = const()[name = string("attn_17_transpose_x_0"), val = bool(false)];
            bool attn_17_transpose_y_0 = const()[name = string("attn_17_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1194_cast_fp16, y = var_1192_cast_fp16)[name = string("attn_17_cast_fp16")];
            tensor<int32, [4]> var_1197 = const()[name = string("op_1197"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_65_cast_fp16 = reshape(shape = var_1197, x = attn_17_cast_fp16)[name = string("input_65_cast_fp16")];
            string obj_35_pad_type_0 = const()[name = string("obj_35_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_35_strides_0 = const()[name = string("obj_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_35_pad_0 = const()[name = string("obj_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_35_dilations_0 = const()[name = string("obj_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_35_groups_0 = const()[name = string("obj_35_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339331520)))];
            tensor<fp16, [1280]> layers_8_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_8_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342608384)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_35_cast_fp16 = conv(bias = layers_8_self_attn_o_proj_bias_to_fp16, dilations = obj_35_dilations_0, groups = obj_35_groups_0, pad = obj_35_pad_0, pad_type = obj_35_pad_type_0, strides = obj_35_strides_0, weight = layers_8_self_attn_o_proj_weight_to_fp16, x = input_65_cast_fp16)[name = string("obj_35_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_35_cast_fp16 = add(x = inputs_33_cast_fp16, y = obj_35_cast_fp16)[name = string("inputs_35_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1215_to_fp16 = const()[name = string("op_1215_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_1215_to_fp16, x = inputs_35_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<fp16, [1280]> input_67_gamma_0_to_fp16 = const()[name = string("input_67_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342611008)))];
            tensor<fp16, [1280]> input_67_beta_0_to_fp16 = const()[name = string("input_67_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342613632)))];
            fp16 input_67_epsilon_0_to_fp16 = const()[name = string("input_67_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_67_cast_fp16 = batch_norm(beta = input_67_beta_0_to_fp16, epsilon = input_67_epsilon_0_to_fp16, gamma = input_67_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_35_cast_fp16)[name = string("input_67_cast_fp16")];
            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_8_fc1_weight_to_fp16 = const()[name = string("layers_8_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342616256)))];
            tensor<fp16, [5120]> layers_8_fc1_bias_to_fp16 = const()[name = string("layers_8_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355723520)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_69_cast_fp16 = conv(bias = layers_8_fc1_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = layers_8_fc1_weight_to_fp16, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")];
            string input_71_mode_0 = const()[name = string("input_71_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            string hidden_states_21_pad_type_0 = const()[name = string("hidden_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_21_strides_0 = const()[name = string("hidden_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = string("hidden_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_21_dilations_0 = const()[name = string("hidden_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_21_groups_0 = const()[name = string("hidden_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_8_fc2_weight_to_fp16 = const()[name = string("layers_8_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355733824)))];
            tensor<fp16, [1280]> layers_8_fc2_bias_to_fp16 = const()[name = string("layers_8_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368841088)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_21_cast_fp16 = conv(bias = layers_8_fc2_bias_to_fp16, dilations = hidden_states_21_dilations_0, groups = hidden_states_21_groups_0, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = hidden_states_21_strides_0, weight = layers_8_fc2_weight_to_fp16, x = input_71_cast_fp16)[name = string("hidden_states_21_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_37_cast_fp16 = add(x = inputs_35_cast_fp16, y = hidden_states_21_cast_fp16)[name = string("inputs_37_cast_fp16")];
            int32 var_1248 = const()[name = string("op_1248"), val = int32(3)];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1267_to_fp16 = const()[name = string("op_1267_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_1267_to_fp16, x = inputs_37_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<fp16, [1280]> obj_37_gamma_0_to_fp16 = const()[name = string("obj_37_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368843712)))];
            tensor<fp16, [1280]> obj_37_beta_0_to_fp16 = const()[name = string("obj_37_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368846336)))];
            fp16 obj_37_epsilon_0_to_fp16 = const()[name = string("obj_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_37_cast_fp16 = batch_norm(beta = obj_37_beta_0_to_fp16, epsilon = obj_37_epsilon_0_to_fp16, gamma = obj_37_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_37_cast_fp16)[name = string("obj_37_cast_fp16")];
            string query_19_pad_type_0 = const()[name = string("query_19_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_19_strides_0 = const()[name = string("query_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_19_pad_0 = const()[name = string("query_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_19_dilations_0 = const()[name = string("query_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_19_groups_0 = const()[name = string("query_19_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368848960)))];
            tensor<fp16, [1280]> layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372125824)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_19_cast_fp16 = conv(bias = layers_9_self_attn_q_proj_bias_to_fp16, dilations = query_19_dilations_0, groups = query_19_groups_0, pad = query_19_pad_0, pad_type = query_19_pad_type_0, strides = query_19_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = obj_37_cast_fp16)[name = string("query_19_cast_fp16")];
            string key_19_pad_type_0 = const()[name = string("key_19_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_19_strides_0 = const()[name = string("key_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_19_pad_0 = const()[name = string("key_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_19_dilations_0 = const()[name = string("key_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_19_groups_0 = const()[name = string("key_19_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372128448)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_19_cast_fp16 = conv(dilations = key_19_dilations_0, groups = key_19_groups_0, pad = key_19_pad_0, pad_type = key_19_pad_type_0, strides = key_19_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = obj_37_cast_fp16)[name = string("key_19_cast_fp16")];
            string value_19_pad_type_0 = const()[name = string("value_19_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_19_strides_0 = const()[name = string("value_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_19_pad_0 = const()[name = string("value_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_19_dilations_0 = const()[name = string("value_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_19_groups_0 = const()[name = string("value_19_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375405312)))];
            tensor<fp16, [1280]> layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378682176)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_19_cast_fp16 = conv(bias = layers_9_self_attn_v_proj_bias_to_fp16, dilations = value_19_dilations_0, groups = value_19_groups_0, pad = value_19_pad_0, pad_type = value_19_pad_type_0, strides = value_19_strides_0, weight = layers_9_self_attn_v_proj_weight_to_fp16, x = obj_37_cast_fp16)[name = string("value_19_cast_fp16")];
            tensor<int32, [4]> var_1302 = const()[name = string("op_1302"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_19_cast_fp16 = reshape(shape = var_1302, x = query_19_cast_fp16)[name = string("mh_q_19_cast_fp16")];
            fp16 var_1304_to_fp16 = const()[name = string("op_1304_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1305_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = var_1304_to_fp16)[name = string("op_1305_cast_fp16")];
            tensor<int32, [4]> var_1306 = const()[name = string("op_1306"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1307_cast_fp16 = reshape(shape = var_1306, x = key_19_cast_fp16)[name = string("op_1307_cast_fp16")];
            bool mh_w_19_transpose_x_0 = const()[name = string("mh_w_19_transpose_x_0"), val = bool(true)];
            bool mh_w_19_transpose_y_0 = const()[name = string("mh_w_19_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_19_cast_fp16 = matmul(transpose_x = mh_w_19_transpose_x_0, transpose_y = mh_w_19_transpose_y_0, x = var_1305_cast_fp16, y = var_1307_cast_fp16)[name = string("mh_w_19_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1310_cast_fp16 = softmax(axis = var_1248, x = mh_w_19_cast_fp16)[name = string("op_1310_cast_fp16")];
            tensor<int32, [4]> var_1311 = const()[name = string("op_1311"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1312_cast_fp16 = reshape(shape = var_1311, x = value_19_cast_fp16)[name = string("op_1312_cast_fp16")];
            bool attn_19_transpose_x_0 = const()[name = string("attn_19_transpose_x_0"), val = bool(false)];
            bool attn_19_transpose_y_0 = const()[name = string("attn_19_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1312_cast_fp16, y = var_1310_cast_fp16)[name = string("attn_19_cast_fp16")];
            tensor<int32, [4]> var_1315 = const()[name = string("op_1315"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_73_cast_fp16 = reshape(shape = var_1315, x = attn_19_cast_fp16)[name = string("input_73_cast_fp16")];
            string obj_39_pad_type_0 = const()[name = string("obj_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_39_strides_0 = const()[name = string("obj_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_39_pad_0 = const()[name = string("obj_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_39_dilations_0 = const()[name = string("obj_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_39_groups_0 = const()[name = string("obj_39_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378684800)))];
            tensor<fp16, [1280]> layers_9_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_9_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381961664)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_39_cast_fp16 = conv(bias = layers_9_self_attn_o_proj_bias_to_fp16, dilations = obj_39_dilations_0, groups = obj_39_groups_0, pad = obj_39_pad_0, pad_type = obj_39_pad_type_0, strides = obj_39_strides_0, weight = layers_9_self_attn_o_proj_weight_to_fp16, x = input_73_cast_fp16)[name = string("obj_39_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_39_cast_fp16 = add(x = inputs_37_cast_fp16, y = obj_39_cast_fp16)[name = string("inputs_39_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1333_to_fp16 = const()[name = string("op_1333_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_1333_to_fp16, x = inputs_39_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<fp16, [1280]> input_75_gamma_0_to_fp16 = const()[name = string("input_75_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381964288)))];
            tensor<fp16, [1280]> input_75_beta_0_to_fp16 = const()[name = string("input_75_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381966912)))];
            fp16 input_75_epsilon_0_to_fp16 = const()[name = string("input_75_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_75_cast_fp16 = batch_norm(beta = input_75_beta_0_to_fp16, epsilon = input_75_epsilon_0_to_fp16, gamma = input_75_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_39_cast_fp16)[name = string("input_75_cast_fp16")];
            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_9_fc1_weight_to_fp16 = const()[name = string("layers_9_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381969536)))];
            tensor<fp16, [5120]> layers_9_fc1_bias_to_fp16 = const()[name = string("layers_9_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395076800)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_77_cast_fp16 = conv(bias = layers_9_fc1_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = layers_9_fc1_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
            string input_79_mode_0 = const()[name = string("input_79_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_79_cast_fp16 = gelu(mode = input_79_mode_0, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_9_fc2_weight_to_fp16 = const()[name = string("layers_9_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395087104)))];
            tensor<fp16, [1280]> layers_9_fc2_bias_to_fp16 = const()[name = string("layers_9_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408194368)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_23_cast_fp16 = conv(bias = layers_9_fc2_bias_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_9_fc2_weight_to_fp16, x = input_79_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_41_cast_fp16 = add(x = inputs_39_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("inputs_41_cast_fp16")];
            int32 var_1366 = const()[name = string("op_1366"), val = int32(3)];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1385_to_fp16 = const()[name = string("op_1385_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_1385_to_fp16, x = inputs_41_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<fp16, [1280]> obj_41_gamma_0_to_fp16 = const()[name = string("obj_41_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408196992)))];
            tensor<fp16, [1280]> obj_41_beta_0_to_fp16 = const()[name = string("obj_41_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408199616)))];
            fp16 obj_41_epsilon_0_to_fp16 = const()[name = string("obj_41_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_41_cast_fp16 = batch_norm(beta = obj_41_beta_0_to_fp16, epsilon = obj_41_epsilon_0_to_fp16, gamma = obj_41_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_41_cast_fp16)[name = string("obj_41_cast_fp16")];
            string query_21_pad_type_0 = const()[name = string("query_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_21_strides_0 = const()[name = string("query_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_21_pad_0 = const()[name = string("query_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_21_dilations_0 = const()[name = string("query_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_21_groups_0 = const()[name = string("query_21_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408202240)))];
            tensor<fp16, [1280]> layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411479104)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_21_cast_fp16 = conv(bias = layers_10_self_attn_q_proj_bias_to_fp16, dilations = query_21_dilations_0, groups = query_21_groups_0, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = query_21_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = obj_41_cast_fp16)[name = string("query_21_cast_fp16")];
            string key_21_pad_type_0 = const()[name = string("key_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_21_strides_0 = const()[name = string("key_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_21_pad_0 = const()[name = string("key_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_21_dilations_0 = const()[name = string("key_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_21_groups_0 = const()[name = string("key_21_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411481728)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_21_cast_fp16 = conv(dilations = key_21_dilations_0, groups = key_21_groups_0, pad = key_21_pad_0, pad_type = key_21_pad_type_0, strides = key_21_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = obj_41_cast_fp16)[name = string("key_21_cast_fp16")];
            string value_21_pad_type_0 = const()[name = string("value_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_21_strides_0 = const()[name = string("value_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_21_pad_0 = const()[name = string("value_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_21_dilations_0 = const()[name = string("value_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_21_groups_0 = const()[name = string("value_21_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414758592)))];
            tensor<fp16, [1280]> layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418035456)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_21_cast_fp16 = conv(bias = layers_10_self_attn_v_proj_bias_to_fp16, dilations = value_21_dilations_0, groups = value_21_groups_0, pad = value_21_pad_0, pad_type = value_21_pad_type_0, strides = value_21_strides_0, weight = layers_10_self_attn_v_proj_weight_to_fp16, x = obj_41_cast_fp16)[name = string("value_21_cast_fp16")];
            tensor<int32, [4]> var_1420 = const()[name = string("op_1420"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_21_cast_fp16 = reshape(shape = var_1420, x = query_21_cast_fp16)[name = string("mh_q_21_cast_fp16")];
            fp16 var_1422_to_fp16 = const()[name = string("op_1422_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1423_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_1422_to_fp16)[name = string("op_1423_cast_fp16")];
            tensor<int32, [4]> var_1424 = const()[name = string("op_1424"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1425_cast_fp16 = reshape(shape = var_1424, x = key_21_cast_fp16)[name = string("op_1425_cast_fp16")];
            bool mh_w_21_transpose_x_0 = const()[name = string("mh_w_21_transpose_x_0"), val = bool(true)];
            bool mh_w_21_transpose_y_0 = const()[name = string("mh_w_21_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_21_cast_fp16 = matmul(transpose_x = mh_w_21_transpose_x_0, transpose_y = mh_w_21_transpose_y_0, x = var_1423_cast_fp16, y = var_1425_cast_fp16)[name = string("mh_w_21_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1428_cast_fp16 = softmax(axis = var_1366, x = mh_w_21_cast_fp16)[name = string("op_1428_cast_fp16")];
            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1430_cast_fp16 = reshape(shape = var_1429, x = value_21_cast_fp16)[name = string("op_1430_cast_fp16")];
            bool attn_21_transpose_x_0 = const()[name = string("attn_21_transpose_x_0"), val = bool(false)];
            bool attn_21_transpose_y_0 = const()[name = string("attn_21_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1430_cast_fp16, y = var_1428_cast_fp16)[name = string("attn_21_cast_fp16")];
            tensor<int32, [4]> var_1433 = const()[name = string("op_1433"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_81_cast_fp16 = reshape(shape = var_1433, x = attn_21_cast_fp16)[name = string("input_81_cast_fp16")];
            string obj_43_pad_type_0 = const()[name = string("obj_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_43_strides_0 = const()[name = string("obj_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_43_pad_0 = const()[name = string("obj_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_43_dilations_0 = const()[name = string("obj_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_43_groups_0 = const()[name = string("obj_43_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418038080)))];
            tensor<fp16, [1280]> layers_10_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_10_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421314944)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_43_cast_fp16 = conv(bias = layers_10_self_attn_o_proj_bias_to_fp16, dilations = obj_43_dilations_0, groups = obj_43_groups_0, pad = obj_43_pad_0, pad_type = obj_43_pad_type_0, strides = obj_43_strides_0, weight = layers_10_self_attn_o_proj_weight_to_fp16, x = input_81_cast_fp16)[name = string("obj_43_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_43_cast_fp16 = add(x = inputs_41_cast_fp16, y = obj_43_cast_fp16)[name = string("inputs_43_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1451_to_fp16 = const()[name = string("op_1451_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_1451_to_fp16, x = inputs_43_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<fp16, [1280]> input_83_gamma_0_to_fp16 = const()[name = string("input_83_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421317568)))];
            tensor<fp16, [1280]> input_83_beta_0_to_fp16 = const()[name = string("input_83_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421320192)))];
            fp16 input_83_epsilon_0_to_fp16 = const()[name = string("input_83_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_83_cast_fp16 = batch_norm(beta = input_83_beta_0_to_fp16, epsilon = input_83_epsilon_0_to_fp16, gamma = input_83_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_43_cast_fp16)[name = string("input_83_cast_fp16")];
            string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_10_fc1_weight_to_fp16 = const()[name = string("layers_10_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421322816)))];
            tensor<fp16, [5120]> layers_10_fc1_bias_to_fp16 = const()[name = string("layers_10_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434430080)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_85_cast_fp16 = conv(bias = layers_10_fc1_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = layers_10_fc1_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
            string input_87_mode_0 = const()[name = string("input_87_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_87_cast_fp16 = gelu(mode = input_87_mode_0, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")];
            string hidden_states_25_pad_type_0 = const()[name = string("hidden_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_25_strides_0 = const()[name = string("hidden_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = string("hidden_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_25_dilations_0 = const()[name = string("hidden_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_25_groups_0 = const()[name = string("hidden_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_10_fc2_weight_to_fp16 = const()[name = string("layers_10_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434440384)))];
            tensor<fp16, [1280]> layers_10_fc2_bias_to_fp16 = const()[name = string("layers_10_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447547648)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_25_cast_fp16 = conv(bias = layers_10_fc2_bias_to_fp16, dilations = hidden_states_25_dilations_0, groups = hidden_states_25_groups_0, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = hidden_states_25_strides_0, weight = layers_10_fc2_weight_to_fp16, x = input_87_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_45_cast_fp16 = add(x = inputs_43_cast_fp16, y = hidden_states_25_cast_fp16)[name = string("inputs_45_cast_fp16")];
            int32 var_1484 = const()[name = string("op_1484"), val = int32(3)];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1503_to_fp16 = const()[name = string("op_1503_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_1503_to_fp16, x = inputs_45_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<fp16, [1280]> obj_45_gamma_0_to_fp16 = const()[name = string("obj_45_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447550272)))];
            tensor<fp16, [1280]> obj_45_beta_0_to_fp16 = const()[name = string("obj_45_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447552896)))];
            fp16 obj_45_epsilon_0_to_fp16 = const()[name = string("obj_45_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_45_cast_fp16 = batch_norm(beta = obj_45_beta_0_to_fp16, epsilon = obj_45_epsilon_0_to_fp16, gamma = obj_45_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_45_cast_fp16)[name = string("obj_45_cast_fp16")];
            string query_23_pad_type_0 = const()[name = string("query_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_23_strides_0 = const()[name = string("query_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_23_pad_0 = const()[name = string("query_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_23_dilations_0 = const()[name = string("query_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_23_groups_0 = const()[name = string("query_23_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_11_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447555520)))];
            tensor<fp16, [1280]> layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450832384)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_23_cast_fp16 = conv(bias = layers_11_self_attn_q_proj_bias_to_fp16, dilations = query_23_dilations_0, groups = query_23_groups_0, pad = query_23_pad_0, pad_type = query_23_pad_type_0, strides = query_23_strides_0, weight = layers_11_self_attn_q_proj_weight_to_fp16, x = obj_45_cast_fp16)[name = string("query_23_cast_fp16")];
            string key_23_pad_type_0 = const()[name = string("key_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_23_strides_0 = const()[name = string("key_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_23_pad_0 = const()[name = string("key_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_23_dilations_0 = const()[name = string("key_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_23_groups_0 = const()[name = string("key_23_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_11_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(450835008)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_23_cast_fp16 = conv(dilations = key_23_dilations_0, groups = key_23_groups_0, pad = key_23_pad_0, pad_type = key_23_pad_type_0, strides = key_23_strides_0, weight = layers_11_self_attn_k_proj_weight_to_fp16, x = obj_45_cast_fp16)[name = string("key_23_cast_fp16")];
            string value_23_pad_type_0 = const()[name = string("value_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_23_strides_0 = const()[name = string("value_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_23_pad_0 = const()[name = string("value_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_23_dilations_0 = const()[name = string("value_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_23_groups_0 = const()[name = string("value_23_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_11_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454111872)))];
            tensor<fp16, [1280]> layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457388736)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_23_cast_fp16 = conv(bias = layers_11_self_attn_v_proj_bias_to_fp16, dilations = value_23_dilations_0, groups = value_23_groups_0, pad = value_23_pad_0, pad_type = value_23_pad_type_0, strides = value_23_strides_0, weight = layers_11_self_attn_v_proj_weight_to_fp16, x = obj_45_cast_fp16)[name = string("value_23_cast_fp16")];
            tensor<int32, [4]> var_1538 = const()[name = string("op_1538"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_23_cast_fp16 = reshape(shape = var_1538, x = query_23_cast_fp16)[name = string("mh_q_23_cast_fp16")];
            fp16 var_1540_to_fp16 = const()[name = string("op_1540_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1541_cast_fp16 = mul(x = mh_q_23_cast_fp16, y = var_1540_to_fp16)[name = string("op_1541_cast_fp16")];
            tensor<int32, [4]> var_1542 = const()[name = string("op_1542"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1543_cast_fp16 = reshape(shape = var_1542, x = key_23_cast_fp16)[name = string("op_1543_cast_fp16")];
            bool mh_w_23_transpose_x_0 = const()[name = string("mh_w_23_transpose_x_0"), val = bool(true)];
            bool mh_w_23_transpose_y_0 = const()[name = string("mh_w_23_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_23_cast_fp16 = matmul(transpose_x = mh_w_23_transpose_x_0, transpose_y = mh_w_23_transpose_y_0, x = var_1541_cast_fp16, y = var_1543_cast_fp16)[name = string("mh_w_23_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1546_cast_fp16 = softmax(axis = var_1484, x = mh_w_23_cast_fp16)[name = string("op_1546_cast_fp16")];
            tensor<int32, [4]> var_1547 = const()[name = string("op_1547"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1548_cast_fp16 = reshape(shape = var_1547, x = value_23_cast_fp16)[name = string("op_1548_cast_fp16")];
            bool attn_23_transpose_x_0 = const()[name = string("attn_23_transpose_x_0"), val = bool(false)];
            bool attn_23_transpose_y_0 = const()[name = string("attn_23_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1548_cast_fp16, y = var_1546_cast_fp16)[name = string("attn_23_cast_fp16")];
            tensor<int32, [4]> var_1551 = const()[name = string("op_1551"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_89_cast_fp16 = reshape(shape = var_1551, x = attn_23_cast_fp16)[name = string("input_89_cast_fp16")];
            string obj_47_pad_type_0 = const()[name = string("obj_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_47_strides_0 = const()[name = string("obj_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_47_pad_0 = const()[name = string("obj_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_47_dilations_0 = const()[name = string("obj_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_47_groups_0 = const()[name = string("obj_47_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_11_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457391360)))];
            tensor<fp16, [1280]> layers_11_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_11_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460668224)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_47_cast_fp16 = conv(bias = layers_11_self_attn_o_proj_bias_to_fp16, dilations = obj_47_dilations_0, groups = obj_47_groups_0, pad = obj_47_pad_0, pad_type = obj_47_pad_type_0, strides = obj_47_strides_0, weight = layers_11_self_attn_o_proj_weight_to_fp16, x = input_89_cast_fp16)[name = string("obj_47_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_47_cast_fp16 = add(x = inputs_45_cast_fp16, y = obj_47_cast_fp16)[name = string("inputs_47_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1569_to_fp16 = const()[name = string("op_1569_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_1569_to_fp16, x = inputs_47_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<fp16, [1280]> input_91_gamma_0_to_fp16 = const()[name = string("input_91_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460670848)))];
            tensor<fp16, [1280]> input_91_beta_0_to_fp16 = const()[name = string("input_91_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460673472)))];
            fp16 input_91_epsilon_0_to_fp16 = const()[name = string("input_91_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_91_cast_fp16 = batch_norm(beta = input_91_beta_0_to_fp16, epsilon = input_91_epsilon_0_to_fp16, gamma = input_91_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_47_cast_fp16)[name = string("input_91_cast_fp16")];
            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_11_fc1_weight_to_fp16 = const()[name = string("layers_11_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460676096)))];
            tensor<fp16, [5120]> layers_11_fc1_bias_to_fp16 = const()[name = string("layers_11_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473783360)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_93_cast_fp16 = conv(bias = layers_11_fc1_bias_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = layers_11_fc1_weight_to_fp16, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")];
            string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_11_fc2_weight_to_fp16 = const()[name = string("layers_11_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473793664)))];
            tensor<fp16, [1280]> layers_11_fc2_bias_to_fp16 = const()[name = string("layers_11_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486900928)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_27_cast_fp16 = conv(bias = layers_11_fc2_bias_to_fp16, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_11_fc2_weight_to_fp16, x = input_95_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_49_cast_fp16 = add(x = inputs_47_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("inputs_49_cast_fp16")];
            int32 var_1602 = const()[name = string("op_1602"), val = int32(3)];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1621_to_fp16 = const()[name = string("op_1621_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_1621_to_fp16, x = inputs_49_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<fp16, [1280]> obj_49_gamma_0_to_fp16 = const()[name = string("obj_49_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486903552)))];
            tensor<fp16, [1280]> obj_49_beta_0_to_fp16 = const()[name = string("obj_49_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486906176)))];
            fp16 obj_49_epsilon_0_to_fp16 = const()[name = string("obj_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_49_cast_fp16 = batch_norm(beta = obj_49_beta_0_to_fp16, epsilon = obj_49_epsilon_0_to_fp16, gamma = obj_49_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_49_cast_fp16)[name = string("obj_49_cast_fp16")];
            string query_25_pad_type_0 = const()[name = string("query_25_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_25_strides_0 = const()[name = string("query_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_25_pad_0 = const()[name = string("query_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_25_dilations_0 = const()[name = string("query_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_25_groups_0 = const()[name = string("query_25_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_12_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486908800)))];
            tensor<fp16, [1280]> layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490185664)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_25_cast_fp16 = conv(bias = layers_12_self_attn_q_proj_bias_to_fp16, dilations = query_25_dilations_0, groups = query_25_groups_0, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = query_25_strides_0, weight = layers_12_self_attn_q_proj_weight_to_fp16, x = obj_49_cast_fp16)[name = string("query_25_cast_fp16")];
            string key_25_pad_type_0 = const()[name = string("key_25_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_25_strides_0 = const()[name = string("key_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_25_pad_0 = const()[name = string("key_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_25_dilations_0 = const()[name = string("key_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_25_groups_0 = const()[name = string("key_25_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_12_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(490188288)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_25_cast_fp16 = conv(dilations = key_25_dilations_0, groups = key_25_groups_0, pad = key_25_pad_0, pad_type = key_25_pad_type_0, strides = key_25_strides_0, weight = layers_12_self_attn_k_proj_weight_to_fp16, x = obj_49_cast_fp16)[name = string("key_25_cast_fp16")];
            string value_25_pad_type_0 = const()[name = string("value_25_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_25_strides_0 = const()[name = string("value_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_25_pad_0 = const()[name = string("value_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_25_dilations_0 = const()[name = string("value_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_25_groups_0 = const()[name = string("value_25_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_12_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493465152)))];
            tensor<fp16, [1280]> layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496742016)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_25_cast_fp16 = conv(bias = layers_12_self_attn_v_proj_bias_to_fp16, dilations = value_25_dilations_0, groups = value_25_groups_0, pad = value_25_pad_0, pad_type = value_25_pad_type_0, strides = value_25_strides_0, weight = layers_12_self_attn_v_proj_weight_to_fp16, x = obj_49_cast_fp16)[name = string("value_25_cast_fp16")];
            tensor<int32, [4]> var_1656 = const()[name = string("op_1656"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_25_cast_fp16 = reshape(shape = var_1656, x = query_25_cast_fp16)[name = string("mh_q_25_cast_fp16")];
            fp16 var_1658_to_fp16 = const()[name = string("op_1658_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1659_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = var_1658_to_fp16)[name = string("op_1659_cast_fp16")];
            tensor<int32, [4]> var_1660 = const()[name = string("op_1660"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1661_cast_fp16 = reshape(shape = var_1660, x = key_25_cast_fp16)[name = string("op_1661_cast_fp16")];
            bool mh_w_25_transpose_x_0 = const()[name = string("mh_w_25_transpose_x_0"), val = bool(true)];
            bool mh_w_25_transpose_y_0 = const()[name = string("mh_w_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1659_cast_fp16, y = var_1661_cast_fp16)[name = string("mh_w_25_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1664_cast_fp16 = softmax(axis = var_1602, x = mh_w_25_cast_fp16)[name = string("op_1664_cast_fp16")];
            tensor<int32, [4]> var_1665 = const()[name = string("op_1665"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1666_cast_fp16 = reshape(shape = var_1665, x = value_25_cast_fp16)[name = string("op_1666_cast_fp16")];
            bool attn_25_transpose_x_0 = const()[name = string("attn_25_transpose_x_0"), val = bool(false)];
            bool attn_25_transpose_y_0 = const()[name = string("attn_25_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1666_cast_fp16, y = var_1664_cast_fp16)[name = string("attn_25_cast_fp16")];
            tensor<int32, [4]> var_1669 = const()[name = string("op_1669"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_97_cast_fp16 = reshape(shape = var_1669, x = attn_25_cast_fp16)[name = string("input_97_cast_fp16")];
            string obj_51_pad_type_0 = const()[name = string("obj_51_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_51_strides_0 = const()[name = string("obj_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_51_pad_0 = const()[name = string("obj_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_51_dilations_0 = const()[name = string("obj_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_51_groups_0 = const()[name = string("obj_51_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_12_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496744640)))];
            tensor<fp16, [1280]> layers_12_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_12_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(500021504)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_51_cast_fp16 = conv(bias = layers_12_self_attn_o_proj_bias_to_fp16, dilations = obj_51_dilations_0, groups = obj_51_groups_0, pad = obj_51_pad_0, pad_type = obj_51_pad_type_0, strides = obj_51_strides_0, weight = layers_12_self_attn_o_proj_weight_to_fp16, x = input_97_cast_fp16)[name = string("obj_51_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_51_cast_fp16 = add(x = inputs_49_cast_fp16, y = obj_51_cast_fp16)[name = string("inputs_51_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1687_to_fp16 = const()[name = string("op_1687_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_1687_to_fp16, x = inputs_51_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<fp16, [1280]> input_99_gamma_0_to_fp16 = const()[name = string("input_99_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(500024128)))];
            tensor<fp16, [1280]> input_99_beta_0_to_fp16 = const()[name = string("input_99_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(500026752)))];
            fp16 input_99_epsilon_0_to_fp16 = const()[name = string("input_99_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_99_cast_fp16 = batch_norm(beta = input_99_beta_0_to_fp16, epsilon = input_99_epsilon_0_to_fp16, gamma = input_99_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_51_cast_fp16)[name = string("input_99_cast_fp16")];
            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_12_fc1_weight_to_fp16 = const()[name = string("layers_12_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(500029376)))];
            tensor<fp16, [5120]> layers_12_fc1_bias_to_fp16 = const()[name = string("layers_12_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513136640)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_101_cast_fp16 = conv(bias = layers_12_fc1_bias_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = layers_12_fc1_weight_to_fp16, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")];
            string input_103_mode_0 = const()[name = string("input_103_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_103_cast_fp16 = gelu(mode = input_103_mode_0, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
            string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_12_fc2_weight_to_fp16 = const()[name = string("layers_12_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513146944)))];
            tensor<fp16, [1280]> layers_12_fc2_bias_to_fp16 = const()[name = string("layers_12_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526254208)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_29_cast_fp16 = conv(bias = layers_12_fc2_bias_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = layers_12_fc2_weight_to_fp16, x = input_103_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_53_cast_fp16 = add(x = inputs_51_cast_fp16, y = hidden_states_29_cast_fp16)[name = string("inputs_53_cast_fp16")];
            int32 var_1720 = const()[name = string("op_1720"), val = int32(3)];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1739_to_fp16 = const()[name = string("op_1739_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_1739_to_fp16, x = inputs_53_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<fp16, [1280]> obj_53_gamma_0_to_fp16 = const()[name = string("obj_53_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526256832)))];
            tensor<fp16, [1280]> obj_53_beta_0_to_fp16 = const()[name = string("obj_53_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526259456)))];
            fp16 obj_53_epsilon_0_to_fp16 = const()[name = string("obj_53_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_53_cast_fp16 = batch_norm(beta = obj_53_beta_0_to_fp16, epsilon = obj_53_epsilon_0_to_fp16, gamma = obj_53_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_53_cast_fp16)[name = string("obj_53_cast_fp16")];
            string query_27_pad_type_0 = const()[name = string("query_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_27_strides_0 = const()[name = string("query_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_27_pad_0 = const()[name = string("query_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_27_dilations_0 = const()[name = string("query_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_27_groups_0 = const()[name = string("query_27_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_13_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526262080)))];
            tensor<fp16, [1280]> layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529538944)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_27_cast_fp16 = conv(bias = layers_13_self_attn_q_proj_bias_to_fp16, dilations = query_27_dilations_0, groups = query_27_groups_0, pad = query_27_pad_0, pad_type = query_27_pad_type_0, strides = query_27_strides_0, weight = layers_13_self_attn_q_proj_weight_to_fp16, x = obj_53_cast_fp16)[name = string("query_27_cast_fp16")];
            string key_27_pad_type_0 = const()[name = string("key_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_27_strides_0 = const()[name = string("key_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_27_pad_0 = const()[name = string("key_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_27_dilations_0 = const()[name = string("key_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_27_groups_0 = const()[name = string("key_27_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_13_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529541568)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_27_cast_fp16 = conv(dilations = key_27_dilations_0, groups = key_27_groups_0, pad = key_27_pad_0, pad_type = key_27_pad_type_0, strides = key_27_strides_0, weight = layers_13_self_attn_k_proj_weight_to_fp16, x = obj_53_cast_fp16)[name = string("key_27_cast_fp16")];
            string value_27_pad_type_0 = const()[name = string("value_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_27_strides_0 = const()[name = string("value_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_27_pad_0 = const()[name = string("value_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_27_dilations_0 = const()[name = string("value_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_27_groups_0 = const()[name = string("value_27_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_13_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(532818432)))];
            tensor<fp16, [1280]> layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536095296)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_27_cast_fp16 = conv(bias = layers_13_self_attn_v_proj_bias_to_fp16, dilations = value_27_dilations_0, groups = value_27_groups_0, pad = value_27_pad_0, pad_type = value_27_pad_type_0, strides = value_27_strides_0, weight = layers_13_self_attn_v_proj_weight_to_fp16, x = obj_53_cast_fp16)[name = string("value_27_cast_fp16")];
            tensor<int32, [4]> var_1774 = const()[name = string("op_1774"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_27_cast_fp16 = reshape(shape = var_1774, x = query_27_cast_fp16)[name = string("mh_q_27_cast_fp16")];
            fp16 var_1776_to_fp16 = const()[name = string("op_1776_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1777_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_1776_to_fp16)[name = string("op_1777_cast_fp16")];
            tensor<int32, [4]> var_1778 = const()[name = string("op_1778"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1779_cast_fp16 = reshape(shape = var_1778, x = key_27_cast_fp16)[name = string("op_1779_cast_fp16")];
            bool mh_w_27_transpose_x_0 = const()[name = string("mh_w_27_transpose_x_0"), val = bool(true)];
            bool mh_w_27_transpose_y_0 = const()[name = string("mh_w_27_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_27_cast_fp16 = matmul(transpose_x = mh_w_27_transpose_x_0, transpose_y = mh_w_27_transpose_y_0, x = var_1777_cast_fp16, y = var_1779_cast_fp16)[name = string("mh_w_27_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1782_cast_fp16 = softmax(axis = var_1720, x = mh_w_27_cast_fp16)[name = string("op_1782_cast_fp16")];
            tensor<int32, [4]> var_1783 = const()[name = string("op_1783"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1784_cast_fp16 = reshape(shape = var_1783, x = value_27_cast_fp16)[name = string("op_1784_cast_fp16")];
            bool attn_27_transpose_x_0 = const()[name = string("attn_27_transpose_x_0"), val = bool(false)];
            bool attn_27_transpose_y_0 = const()[name = string("attn_27_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1784_cast_fp16, y = var_1782_cast_fp16)[name = string("attn_27_cast_fp16")];
            tensor<int32, [4]> var_1787 = const()[name = string("op_1787"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_105_cast_fp16 = reshape(shape = var_1787, x = attn_27_cast_fp16)[name = string("input_105_cast_fp16")];
            string obj_55_pad_type_0 = const()[name = string("obj_55_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_55_strides_0 = const()[name = string("obj_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_55_pad_0 = const()[name = string("obj_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_55_dilations_0 = const()[name = string("obj_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_55_groups_0 = const()[name = string("obj_55_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_13_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536097920)))];
            tensor<fp16, [1280]> layers_13_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_13_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539374784)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_55_cast_fp16 = conv(bias = layers_13_self_attn_o_proj_bias_to_fp16, dilations = obj_55_dilations_0, groups = obj_55_groups_0, pad = obj_55_pad_0, pad_type = obj_55_pad_type_0, strides = obj_55_strides_0, weight = layers_13_self_attn_o_proj_weight_to_fp16, x = input_105_cast_fp16)[name = string("obj_55_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_55_cast_fp16 = add(x = inputs_53_cast_fp16, y = obj_55_cast_fp16)[name = string("inputs_55_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1805_to_fp16 = const()[name = string("op_1805_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_1805_to_fp16, x = inputs_55_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<fp16, [1280]> input_107_gamma_0_to_fp16 = const()[name = string("input_107_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539377408)))];
            tensor<fp16, [1280]> input_107_beta_0_to_fp16 = const()[name = string("input_107_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539380032)))];
            fp16 input_107_epsilon_0_to_fp16 = const()[name = string("input_107_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_107_cast_fp16 = batch_norm(beta = input_107_beta_0_to_fp16, epsilon = input_107_epsilon_0_to_fp16, gamma = input_107_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_55_cast_fp16)[name = string("input_107_cast_fp16")];
            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_13_fc1_weight_to_fp16 = const()[name = string("layers_13_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539382656)))];
            tensor<fp16, [5120]> layers_13_fc1_bias_to_fp16 = const()[name = string("layers_13_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552489920)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_109_cast_fp16 = conv(bias = layers_13_fc1_bias_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = layers_13_fc1_weight_to_fp16, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")];
            string input_111_mode_0 = const()[name = string("input_111_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_111_cast_fp16 = gelu(mode = input_111_mode_0, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
            string hidden_states_31_pad_type_0 = const()[name = string("hidden_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_31_strides_0 = const()[name = string("hidden_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = string("hidden_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_31_dilations_0 = const()[name = string("hidden_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_31_groups_0 = const()[name = string("hidden_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_13_fc2_weight_to_fp16 = const()[name = string("layers_13_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552500224)))];
            tensor<fp16, [1280]> layers_13_fc2_bias_to_fp16 = const()[name = string("layers_13_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565607488)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_31_cast_fp16 = conv(bias = layers_13_fc2_bias_to_fp16, dilations = hidden_states_31_dilations_0, groups = hidden_states_31_groups_0, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = hidden_states_31_strides_0, weight = layers_13_fc2_weight_to_fp16, x = input_111_cast_fp16)[name = string("hidden_states_31_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_57_cast_fp16 = add(x = inputs_55_cast_fp16, y = hidden_states_31_cast_fp16)[name = string("inputs_57_cast_fp16")];
            int32 var_1838 = const()[name = string("op_1838"), val = int32(3)];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1857_to_fp16 = const()[name = string("op_1857_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_1857_to_fp16, x = inputs_57_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<fp16, [1280]> obj_57_gamma_0_to_fp16 = const()[name = string("obj_57_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565610112)))];
            tensor<fp16, [1280]> obj_57_beta_0_to_fp16 = const()[name = string("obj_57_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565612736)))];
            fp16 obj_57_epsilon_0_to_fp16 = const()[name = string("obj_57_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_57_cast_fp16 = batch_norm(beta = obj_57_beta_0_to_fp16, epsilon = obj_57_epsilon_0_to_fp16, gamma = obj_57_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_57_cast_fp16)[name = string("obj_57_cast_fp16")];
            string query_29_pad_type_0 = const()[name = string("query_29_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_29_strides_0 = const()[name = string("query_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_29_pad_0 = const()[name = string("query_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_29_dilations_0 = const()[name = string("query_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_29_groups_0 = const()[name = string("query_29_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565615360)))];
            tensor<fp16, [1280]> layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(568892224)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_29_cast_fp16 = conv(bias = layers_14_self_attn_q_proj_bias_to_fp16, dilations = query_29_dilations_0, groups = query_29_groups_0, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = query_29_strides_0, weight = layers_14_self_attn_q_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = string("query_29_cast_fp16")];
            string key_29_pad_type_0 = const()[name = string("key_29_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_29_strides_0 = const()[name = string("key_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_29_pad_0 = const()[name = string("key_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_29_dilations_0 = const()[name = string("key_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_29_groups_0 = const()[name = string("key_29_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(568894848)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_29_cast_fp16 = conv(dilations = key_29_dilations_0, groups = key_29_groups_0, pad = key_29_pad_0, pad_type = key_29_pad_type_0, strides = key_29_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = string("key_29_cast_fp16")];
            string value_29_pad_type_0 = const()[name = string("value_29_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_29_strides_0 = const()[name = string("value_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_29_pad_0 = const()[name = string("value_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_29_dilations_0 = const()[name = string("value_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_29_groups_0 = const()[name = string("value_29_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(572171712)))];
            tensor<fp16, [1280]> layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(575448576)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_29_cast_fp16 = conv(bias = layers_14_self_attn_v_proj_bias_to_fp16, dilations = value_29_dilations_0, groups = value_29_groups_0, pad = value_29_pad_0, pad_type = value_29_pad_type_0, strides = value_29_strides_0, weight = layers_14_self_attn_v_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = string("value_29_cast_fp16")];
            tensor<int32, [4]> var_1892 = const()[name = string("op_1892"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_29_cast_fp16 = reshape(shape = var_1892, x = query_29_cast_fp16)[name = string("mh_q_29_cast_fp16")];
            fp16 var_1894_to_fp16 = const()[name = string("op_1894_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_1895_cast_fp16 = mul(x = mh_q_29_cast_fp16, y = var_1894_to_fp16)[name = string("op_1895_cast_fp16")];
            tensor<int32, [4]> var_1896 = const()[name = string("op_1896"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1897_cast_fp16 = reshape(shape = var_1896, x = key_29_cast_fp16)[name = string("op_1897_cast_fp16")];
            bool mh_w_29_transpose_x_0 = const()[name = string("mh_w_29_transpose_x_0"), val = bool(true)];
            bool mh_w_29_transpose_y_0 = const()[name = string("mh_w_29_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_1895_cast_fp16, y = var_1897_cast_fp16)[name = string("mh_w_29_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_1900_cast_fp16 = softmax(axis = var_1838, x = mh_w_29_cast_fp16)[name = string("op_1900_cast_fp16")];
            tensor<int32, [4]> var_1901 = const()[name = string("op_1901"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_1902_cast_fp16 = reshape(shape = var_1901, x = value_29_cast_fp16)[name = string("op_1902_cast_fp16")];
            bool attn_29_transpose_x_0 = const()[name = string("attn_29_transpose_x_0"), val = bool(false)];
            bool attn_29_transpose_y_0 = const()[name = string("attn_29_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1902_cast_fp16, y = var_1900_cast_fp16)[name = string("attn_29_cast_fp16")];
            tensor<int32, [4]> var_1905 = const()[name = string("op_1905"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_113_cast_fp16 = reshape(shape = var_1905, x = attn_29_cast_fp16)[name = string("input_113_cast_fp16")];
            string obj_59_pad_type_0 = const()[name = string("obj_59_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_59_strides_0 = const()[name = string("obj_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_59_pad_0 = const()[name = string("obj_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_59_dilations_0 = const()[name = string("obj_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_59_groups_0 = const()[name = string("obj_59_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(575451200)))];
            tensor<fp16, [1280]> layers_14_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_14_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(578728064)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_59_cast_fp16 = conv(bias = layers_14_self_attn_o_proj_bias_to_fp16, dilations = obj_59_dilations_0, groups = obj_59_groups_0, pad = obj_59_pad_0, pad_type = obj_59_pad_type_0, strides = obj_59_strides_0, weight = layers_14_self_attn_o_proj_weight_to_fp16, x = input_113_cast_fp16)[name = string("obj_59_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_59_cast_fp16 = add(x = inputs_57_cast_fp16, y = obj_59_cast_fp16)[name = string("inputs_59_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1923_to_fp16 = const()[name = string("op_1923_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_1923_to_fp16, x = inputs_59_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<fp16, [1280]> input_115_gamma_0_to_fp16 = const()[name = string("input_115_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(578730688)))];
            tensor<fp16, [1280]> input_115_beta_0_to_fp16 = const()[name = string("input_115_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(578733312)))];
            fp16 input_115_epsilon_0_to_fp16 = const()[name = string("input_115_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_115_cast_fp16 = batch_norm(beta = input_115_beta_0_to_fp16, epsilon = input_115_epsilon_0_to_fp16, gamma = input_115_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_59_cast_fp16)[name = string("input_115_cast_fp16")];
            string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_14_fc1_weight_to_fp16 = const()[name = string("layers_14_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(578735936)))];
            tensor<fp16, [5120]> layers_14_fc1_bias_to_fp16 = const()[name = string("layers_14_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591843200)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_117_cast_fp16 = conv(bias = layers_14_fc1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = layers_14_fc1_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
            string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_119_cast_fp16 = gelu(mode = input_119_mode_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_14_fc2_weight_to_fp16 = const()[name = string("layers_14_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591853504)))];
            tensor<fp16, [1280]> layers_14_fc2_bias_to_fp16 = const()[name = string("layers_14_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604960768)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_33_cast_fp16 = conv(bias = layers_14_fc2_bias_to_fp16, dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_14_fc2_weight_to_fp16, x = input_119_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_61_cast_fp16 = add(x = inputs_59_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("inputs_61_cast_fp16")];
            int32 var_1956 = const()[name = string("op_1956"), val = int32(3)];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1975_to_fp16 = const()[name = string("op_1975_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_1975_to_fp16, x = inputs_61_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<fp16, [1280]> obj_61_gamma_0_to_fp16 = const()[name = string("obj_61_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604963392)))];
            tensor<fp16, [1280]> obj_61_beta_0_to_fp16 = const()[name = string("obj_61_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604966016)))];
            fp16 obj_61_epsilon_0_to_fp16 = const()[name = string("obj_61_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_61_cast_fp16 = batch_norm(beta = obj_61_beta_0_to_fp16, epsilon = obj_61_epsilon_0_to_fp16, gamma = obj_61_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_61_cast_fp16)[name = string("obj_61_cast_fp16")];
            string query_31_pad_type_0 = const()[name = string("query_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_31_strides_0 = const()[name = string("query_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_31_pad_0 = const()[name = string("query_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_31_dilations_0 = const()[name = string("query_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_31_groups_0 = const()[name = string("query_31_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_15_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604968640)))];
            tensor<fp16, [1280]> layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(608245504)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_31_cast_fp16 = conv(bias = layers_15_self_attn_q_proj_bias_to_fp16, dilations = query_31_dilations_0, groups = query_31_groups_0, pad = query_31_pad_0, pad_type = query_31_pad_type_0, strides = query_31_strides_0, weight = layers_15_self_attn_q_proj_weight_to_fp16, x = obj_61_cast_fp16)[name = string("query_31_cast_fp16")];
            string key_31_pad_type_0 = const()[name = string("key_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_31_strides_0 = const()[name = string("key_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_31_pad_0 = const()[name = string("key_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_31_dilations_0 = const()[name = string("key_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_31_groups_0 = const()[name = string("key_31_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_15_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(608248128)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_31_cast_fp16 = conv(dilations = key_31_dilations_0, groups = key_31_groups_0, pad = key_31_pad_0, pad_type = key_31_pad_type_0, strides = key_31_strides_0, weight = layers_15_self_attn_k_proj_weight_to_fp16, x = obj_61_cast_fp16)[name = string("key_31_cast_fp16")];
            string value_31_pad_type_0 = const()[name = string("value_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_31_strides_0 = const()[name = string("value_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_31_pad_0 = const()[name = string("value_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_31_dilations_0 = const()[name = string("value_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_31_groups_0 = const()[name = string("value_31_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_15_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(611524992)))];
            tensor<fp16, [1280]> layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(614801856)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_31_cast_fp16 = conv(bias = layers_15_self_attn_v_proj_bias_to_fp16, dilations = value_31_dilations_0, groups = value_31_groups_0, pad = value_31_pad_0, pad_type = value_31_pad_type_0, strides = value_31_strides_0, weight = layers_15_self_attn_v_proj_weight_to_fp16, x = obj_61_cast_fp16)[name = string("value_31_cast_fp16")];
            tensor<int32, [4]> var_2010 = const()[name = string("op_2010"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_31_cast_fp16 = reshape(shape = var_2010, x = query_31_cast_fp16)[name = string("mh_q_31_cast_fp16")];
            fp16 var_2012_to_fp16 = const()[name = string("op_2012_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2013_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = var_2012_to_fp16)[name = string("op_2013_cast_fp16")];
            tensor<int32, [4]> var_2014 = const()[name = string("op_2014"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2015_cast_fp16 = reshape(shape = var_2014, x = key_31_cast_fp16)[name = string("op_2015_cast_fp16")];
            bool mh_w_31_transpose_x_0 = const()[name = string("mh_w_31_transpose_x_0"), val = bool(true)];
            bool mh_w_31_transpose_y_0 = const()[name = string("mh_w_31_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_31_cast_fp16 = matmul(transpose_x = mh_w_31_transpose_x_0, transpose_y = mh_w_31_transpose_y_0, x = var_2013_cast_fp16, y = var_2015_cast_fp16)[name = string("mh_w_31_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2018_cast_fp16 = softmax(axis = var_1956, x = mh_w_31_cast_fp16)[name = string("op_2018_cast_fp16")];
            tensor<int32, [4]> var_2019 = const()[name = string("op_2019"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2020_cast_fp16 = reshape(shape = var_2019, x = value_31_cast_fp16)[name = string("op_2020_cast_fp16")];
            bool attn_31_transpose_x_0 = const()[name = string("attn_31_transpose_x_0"), val = bool(false)];
            bool attn_31_transpose_y_0 = const()[name = string("attn_31_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2020_cast_fp16, y = var_2018_cast_fp16)[name = string("attn_31_cast_fp16")];
            tensor<int32, [4]> var_2023 = const()[name = string("op_2023"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_121_cast_fp16 = reshape(shape = var_2023, x = attn_31_cast_fp16)[name = string("input_121_cast_fp16")];
            string obj_63_pad_type_0 = const()[name = string("obj_63_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_63_strides_0 = const()[name = string("obj_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_63_pad_0 = const()[name = string("obj_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_63_dilations_0 = const()[name = string("obj_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_63_groups_0 = const()[name = string("obj_63_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_15_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(614804480)))];
            tensor<fp16, [1280]> layers_15_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_15_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(618081344)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_63_cast_fp16 = conv(bias = layers_15_self_attn_o_proj_bias_to_fp16, dilations = obj_63_dilations_0, groups = obj_63_groups_0, pad = obj_63_pad_0, pad_type = obj_63_pad_type_0, strides = obj_63_strides_0, weight = layers_15_self_attn_o_proj_weight_to_fp16, x = input_121_cast_fp16)[name = string("obj_63_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_63_cast_fp16 = add(x = inputs_61_cast_fp16, y = obj_63_cast_fp16)[name = string("inputs_63_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2041_to_fp16 = const()[name = string("op_2041_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_2041_to_fp16, x = inputs_63_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<fp16, [1280]> input_123_gamma_0_to_fp16 = const()[name = string("input_123_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(618083968)))];
            tensor<fp16, [1280]> input_123_beta_0_to_fp16 = const()[name = string("input_123_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(618086592)))];
            fp16 input_123_epsilon_0_to_fp16 = const()[name = string("input_123_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_123_cast_fp16 = batch_norm(beta = input_123_beta_0_to_fp16, epsilon = input_123_epsilon_0_to_fp16, gamma = input_123_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_63_cast_fp16)[name = string("input_123_cast_fp16")];
            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_15_fc1_weight_to_fp16 = const()[name = string("layers_15_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(618089216)))];
            tensor<fp16, [5120]> layers_15_fc1_bias_to_fp16 = const()[name = string("layers_15_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(631196480)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_125_cast_fp16 = conv(bias = layers_15_fc1_bias_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = layers_15_fc1_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")];
            string input_127_mode_0 = const()[name = string("input_127_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_127_cast_fp16 = gelu(mode = input_127_mode_0, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")];
            string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_15_fc2_weight_to_fp16 = const()[name = string("layers_15_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(631206784)))];
            tensor<fp16, [1280]> layers_15_fc2_bias_to_fp16 = const()[name = string("layers_15_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(644314048)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_35_cast_fp16 = conv(bias = layers_15_fc2_bias_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = layers_15_fc2_weight_to_fp16, x = input_127_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_65_cast_fp16 = add(x = inputs_63_cast_fp16, y = hidden_states_35_cast_fp16)[name = string("inputs_65_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(3)];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2093_to_fp16 = const()[name = string("op_2093_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_2093_to_fp16, x = inputs_65_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<fp16, [1280]> obj_65_gamma_0_to_fp16 = const()[name = string("obj_65_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(644316672)))];
            tensor<fp16, [1280]> obj_65_beta_0_to_fp16 = const()[name = string("obj_65_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(644319296)))];
            fp16 obj_65_epsilon_0_to_fp16 = const()[name = string("obj_65_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_65_cast_fp16 = batch_norm(beta = obj_65_beta_0_to_fp16, epsilon = obj_65_epsilon_0_to_fp16, gamma = obj_65_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_65_cast_fp16)[name = string("obj_65_cast_fp16")];
            string query_33_pad_type_0 = const()[name = string("query_33_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_33_strides_0 = const()[name = string("query_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_33_pad_0 = const()[name = string("query_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_33_dilations_0 = const()[name = string("query_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_33_groups_0 = const()[name = string("query_33_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_16_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(644321920)))];
            tensor<fp16, [1280]> layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647598784)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_33_cast_fp16 = conv(bias = layers_16_self_attn_q_proj_bias_to_fp16, dilations = query_33_dilations_0, groups = query_33_groups_0, pad = query_33_pad_0, pad_type = query_33_pad_type_0, strides = query_33_strides_0, weight = layers_16_self_attn_q_proj_weight_to_fp16, x = obj_65_cast_fp16)[name = string("query_33_cast_fp16")];
            string key_33_pad_type_0 = const()[name = string("key_33_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_33_strides_0 = const()[name = string("key_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_33_pad_0 = const()[name = string("key_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_33_dilations_0 = const()[name = string("key_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_33_groups_0 = const()[name = string("key_33_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_16_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647601408)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_33_cast_fp16 = conv(dilations = key_33_dilations_0, groups = key_33_groups_0, pad = key_33_pad_0, pad_type = key_33_pad_type_0, strides = key_33_strides_0, weight = layers_16_self_attn_k_proj_weight_to_fp16, x = obj_65_cast_fp16)[name = string("key_33_cast_fp16")];
            string value_33_pad_type_0 = const()[name = string("value_33_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_33_strides_0 = const()[name = string("value_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_33_pad_0 = const()[name = string("value_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_33_dilations_0 = const()[name = string("value_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_33_groups_0 = const()[name = string("value_33_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_16_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(650878272)))];
            tensor<fp16, [1280]> layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(654155136)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_33_cast_fp16 = conv(bias = layers_16_self_attn_v_proj_bias_to_fp16, dilations = value_33_dilations_0, groups = value_33_groups_0, pad = value_33_pad_0, pad_type = value_33_pad_type_0, strides = value_33_strides_0, weight = layers_16_self_attn_v_proj_weight_to_fp16, x = obj_65_cast_fp16)[name = string("value_33_cast_fp16")];
            tensor<int32, [4]> var_2128 = const()[name = string("op_2128"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_33_cast_fp16 = reshape(shape = var_2128, x = query_33_cast_fp16)[name = string("mh_q_33_cast_fp16")];
            fp16 var_2130_to_fp16 = const()[name = string("op_2130_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2131_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_2130_to_fp16)[name = string("op_2131_cast_fp16")];
            tensor<int32, [4]> var_2132 = const()[name = string("op_2132"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2133_cast_fp16 = reshape(shape = var_2132, x = key_33_cast_fp16)[name = string("op_2133_cast_fp16")];
            bool mh_w_33_transpose_x_0 = const()[name = string("mh_w_33_transpose_x_0"), val = bool(true)];
            bool mh_w_33_transpose_y_0 = const()[name = string("mh_w_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_33_cast_fp16 = matmul(transpose_x = mh_w_33_transpose_x_0, transpose_y = mh_w_33_transpose_y_0, x = var_2131_cast_fp16, y = var_2133_cast_fp16)[name = string("mh_w_33_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2136_cast_fp16 = softmax(axis = var_2074, x = mh_w_33_cast_fp16)[name = string("op_2136_cast_fp16")];
            tensor<int32, [4]> var_2137 = const()[name = string("op_2137"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2138_cast_fp16 = reshape(shape = var_2137, x = value_33_cast_fp16)[name = string("op_2138_cast_fp16")];
            bool attn_33_transpose_x_0 = const()[name = string("attn_33_transpose_x_0"), val = bool(false)];
            bool attn_33_transpose_y_0 = const()[name = string("attn_33_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2138_cast_fp16, y = var_2136_cast_fp16)[name = string("attn_33_cast_fp16")];
            tensor<int32, [4]> var_2141 = const()[name = string("op_2141"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_129_cast_fp16 = reshape(shape = var_2141, x = attn_33_cast_fp16)[name = string("input_129_cast_fp16")];
            string obj_67_pad_type_0 = const()[name = string("obj_67_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_67_strides_0 = const()[name = string("obj_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_67_pad_0 = const()[name = string("obj_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_67_dilations_0 = const()[name = string("obj_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_67_groups_0 = const()[name = string("obj_67_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_16_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(654157760)))];
            tensor<fp16, [1280]> layers_16_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_16_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(657434624)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_67_cast_fp16 = conv(bias = layers_16_self_attn_o_proj_bias_to_fp16, dilations = obj_67_dilations_0, groups = obj_67_groups_0, pad = obj_67_pad_0, pad_type = obj_67_pad_type_0, strides = obj_67_strides_0, weight = layers_16_self_attn_o_proj_weight_to_fp16, x = input_129_cast_fp16)[name = string("obj_67_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_67_cast_fp16 = add(x = inputs_65_cast_fp16, y = obj_67_cast_fp16)[name = string("inputs_67_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2159_to_fp16 = const()[name = string("op_2159_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_2159_to_fp16, x = inputs_67_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<fp16, [1280]> input_131_gamma_0_to_fp16 = const()[name = string("input_131_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(657437248)))];
            tensor<fp16, [1280]> input_131_beta_0_to_fp16 = const()[name = string("input_131_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(657439872)))];
            fp16 input_131_epsilon_0_to_fp16 = const()[name = string("input_131_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_131_cast_fp16 = batch_norm(beta = input_131_beta_0_to_fp16, epsilon = input_131_epsilon_0_to_fp16, gamma = input_131_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_67_cast_fp16)[name = string("input_131_cast_fp16")];
            string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_16_fc1_weight_to_fp16 = const()[name = string("layers_16_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(657442496)))];
            tensor<fp16, [5120]> layers_16_fc1_bias_to_fp16 = const()[name = string("layers_16_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(670549760)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_133_cast_fp16 = conv(bias = layers_16_fc1_bias_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = layers_16_fc1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
            string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_16_fc2_weight_to_fp16 = const()[name = string("layers_16_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(670560064)))];
            tensor<fp16, [1280]> layers_16_fc2_bias_to_fp16 = const()[name = string("layers_16_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683667328)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_37_cast_fp16 = conv(bias = layers_16_fc2_bias_to_fp16, dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_16_fc2_weight_to_fp16, x = input_135_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_69_cast_fp16 = add(x = inputs_67_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("inputs_69_cast_fp16")];
            int32 var_2192 = const()[name = string("op_2192"), val = int32(3)];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2211_to_fp16 = const()[name = string("op_2211_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_2211_to_fp16, x = inputs_69_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<fp16, [1280]> obj_69_gamma_0_to_fp16 = const()[name = string("obj_69_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683669952)))];
            tensor<fp16, [1280]> obj_69_beta_0_to_fp16 = const()[name = string("obj_69_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683672576)))];
            fp16 obj_69_epsilon_0_to_fp16 = const()[name = string("obj_69_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_69_cast_fp16 = batch_norm(beta = obj_69_beta_0_to_fp16, epsilon = obj_69_epsilon_0_to_fp16, gamma = obj_69_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_69_cast_fp16)[name = string("obj_69_cast_fp16")];
            string query_35_pad_type_0 = const()[name = string("query_35_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_35_strides_0 = const()[name = string("query_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_35_pad_0 = const()[name = string("query_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_35_dilations_0 = const()[name = string("query_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_35_groups_0 = const()[name = string("query_35_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_17_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683675200)))];
            tensor<fp16, [1280]> layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686952064)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_35_cast_fp16 = conv(bias = layers_17_self_attn_q_proj_bias_to_fp16, dilations = query_35_dilations_0, groups = query_35_groups_0, pad = query_35_pad_0, pad_type = query_35_pad_type_0, strides = query_35_strides_0, weight = layers_17_self_attn_q_proj_weight_to_fp16, x = obj_69_cast_fp16)[name = string("query_35_cast_fp16")];
            string key_35_pad_type_0 = const()[name = string("key_35_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_35_strides_0 = const()[name = string("key_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_35_pad_0 = const()[name = string("key_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_35_dilations_0 = const()[name = string("key_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_35_groups_0 = const()[name = string("key_35_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_17_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686954688)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_35_cast_fp16 = conv(dilations = key_35_dilations_0, groups = key_35_groups_0, pad = key_35_pad_0, pad_type = key_35_pad_type_0, strides = key_35_strides_0, weight = layers_17_self_attn_k_proj_weight_to_fp16, x = obj_69_cast_fp16)[name = string("key_35_cast_fp16")];
            string value_35_pad_type_0 = const()[name = string("value_35_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_35_strides_0 = const()[name = string("value_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_35_pad_0 = const()[name = string("value_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_35_dilations_0 = const()[name = string("value_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_35_groups_0 = const()[name = string("value_35_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_17_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690231552)))];
            tensor<fp16, [1280]> layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(693508416)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_35_cast_fp16 = conv(bias = layers_17_self_attn_v_proj_bias_to_fp16, dilations = value_35_dilations_0, groups = value_35_groups_0, pad = value_35_pad_0, pad_type = value_35_pad_type_0, strides = value_35_strides_0, weight = layers_17_self_attn_v_proj_weight_to_fp16, x = obj_69_cast_fp16)[name = string("value_35_cast_fp16")];
            tensor<int32, [4]> var_2246 = const()[name = string("op_2246"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_35_cast_fp16 = reshape(shape = var_2246, x = query_35_cast_fp16)[name = string("mh_q_35_cast_fp16")];
            fp16 var_2248_to_fp16 = const()[name = string("op_2248_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2249_cast_fp16 = mul(x = mh_q_35_cast_fp16, y = var_2248_to_fp16)[name = string("op_2249_cast_fp16")];
            tensor<int32, [4]> var_2250 = const()[name = string("op_2250"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2251_cast_fp16 = reshape(shape = var_2250, x = key_35_cast_fp16)[name = string("op_2251_cast_fp16")];
            bool mh_w_35_transpose_x_0 = const()[name = string("mh_w_35_transpose_x_0"), val = bool(true)];
            bool mh_w_35_transpose_y_0 = const()[name = string("mh_w_35_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_35_cast_fp16 = matmul(transpose_x = mh_w_35_transpose_x_0, transpose_y = mh_w_35_transpose_y_0, x = var_2249_cast_fp16, y = var_2251_cast_fp16)[name = string("mh_w_35_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2254_cast_fp16 = softmax(axis = var_2192, x = mh_w_35_cast_fp16)[name = string("op_2254_cast_fp16")];
            tensor<int32, [4]> var_2255 = const()[name = string("op_2255"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2256_cast_fp16 = reshape(shape = var_2255, x = value_35_cast_fp16)[name = string("op_2256_cast_fp16")];
            bool attn_35_transpose_x_0 = const()[name = string("attn_35_transpose_x_0"), val = bool(false)];
            bool attn_35_transpose_y_0 = const()[name = string("attn_35_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2256_cast_fp16, y = var_2254_cast_fp16)[name = string("attn_35_cast_fp16")];
            tensor<int32, [4]> var_2259 = const()[name = string("op_2259"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_137_cast_fp16 = reshape(shape = var_2259, x = attn_35_cast_fp16)[name = string("input_137_cast_fp16")];
            string obj_71_pad_type_0 = const()[name = string("obj_71_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_71_strides_0 = const()[name = string("obj_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_71_pad_0 = const()[name = string("obj_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_71_dilations_0 = const()[name = string("obj_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_71_groups_0 = const()[name = string("obj_71_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_17_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(693511040)))];
            tensor<fp16, [1280]> layers_17_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_17_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(696787904)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_71_cast_fp16 = conv(bias = layers_17_self_attn_o_proj_bias_to_fp16, dilations = obj_71_dilations_0, groups = obj_71_groups_0, pad = obj_71_pad_0, pad_type = obj_71_pad_type_0, strides = obj_71_strides_0, weight = layers_17_self_attn_o_proj_weight_to_fp16, x = input_137_cast_fp16)[name = string("obj_71_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_71_cast_fp16 = add(x = inputs_69_cast_fp16, y = obj_71_cast_fp16)[name = string("inputs_71_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2277_to_fp16 = const()[name = string("op_2277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_2277_to_fp16, x = inputs_71_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<fp16, [1280]> input_139_gamma_0_to_fp16 = const()[name = string("input_139_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(696790528)))];
            tensor<fp16, [1280]> input_139_beta_0_to_fp16 = const()[name = string("input_139_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(696793152)))];
            fp16 input_139_epsilon_0_to_fp16 = const()[name = string("input_139_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_139_cast_fp16 = batch_norm(beta = input_139_beta_0_to_fp16, epsilon = input_139_epsilon_0_to_fp16, gamma = input_139_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_71_cast_fp16)[name = string("input_139_cast_fp16")];
            string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_17_fc1_weight_to_fp16 = const()[name = string("layers_17_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(696795776)))];
            tensor<fp16, [5120]> layers_17_fc1_bias_to_fp16 = const()[name = string("layers_17_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(709903040)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_141_cast_fp16 = conv(bias = layers_17_fc1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = layers_17_fc1_weight_to_fp16, x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
            string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_143_cast_fp16 = gelu(mode = input_143_mode_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")];
            string hidden_states_39_pad_type_0 = const()[name = string("hidden_states_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_39_strides_0 = const()[name = string("hidden_states_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = string("hidden_states_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_39_dilations_0 = const()[name = string("hidden_states_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_39_groups_0 = const()[name = string("hidden_states_39_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_17_fc2_weight_to_fp16 = const()[name = string("layers_17_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(709913344)))];
            tensor<fp16, [1280]> layers_17_fc2_bias_to_fp16 = const()[name = string("layers_17_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723020608)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_39_cast_fp16 = conv(bias = layers_17_fc2_bias_to_fp16, dilations = hidden_states_39_dilations_0, groups = hidden_states_39_groups_0, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = hidden_states_39_strides_0, weight = layers_17_fc2_weight_to_fp16, x = input_143_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_73_cast_fp16 = add(x = inputs_71_cast_fp16, y = hidden_states_39_cast_fp16)[name = string("inputs_73_cast_fp16")];
            int32 var_2310 = const()[name = string("op_2310"), val = int32(3)];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2329_to_fp16 = const()[name = string("op_2329_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_2329_to_fp16, x = inputs_73_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<fp16, [1280]> obj_73_gamma_0_to_fp16 = const()[name = string("obj_73_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723023232)))];
            tensor<fp16, [1280]> obj_73_beta_0_to_fp16 = const()[name = string("obj_73_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723025856)))];
            fp16 obj_73_epsilon_0_to_fp16 = const()[name = string("obj_73_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_73_cast_fp16 = batch_norm(beta = obj_73_beta_0_to_fp16, epsilon = obj_73_epsilon_0_to_fp16, gamma = obj_73_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_73_cast_fp16)[name = string("obj_73_cast_fp16")];
            string query_37_pad_type_0 = const()[name = string("query_37_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_37_strides_0 = const()[name = string("query_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_37_pad_0 = const()[name = string("query_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_37_dilations_0 = const()[name = string("query_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_37_groups_0 = const()[name = string("query_37_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_18_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723028480)))];
            tensor<fp16, [1280]> layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726305344)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_37_cast_fp16 = conv(bias = layers_18_self_attn_q_proj_bias_to_fp16, dilations = query_37_dilations_0, groups = query_37_groups_0, pad = query_37_pad_0, pad_type = query_37_pad_type_0, strides = query_37_strides_0, weight = layers_18_self_attn_q_proj_weight_to_fp16, x = obj_73_cast_fp16)[name = string("query_37_cast_fp16")];
            string key_37_pad_type_0 = const()[name = string("key_37_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_37_strides_0 = const()[name = string("key_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_37_pad_0 = const()[name = string("key_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_37_dilations_0 = const()[name = string("key_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_37_groups_0 = const()[name = string("key_37_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_18_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726307968)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_37_cast_fp16 = conv(dilations = key_37_dilations_0, groups = key_37_groups_0, pad = key_37_pad_0, pad_type = key_37_pad_type_0, strides = key_37_strides_0, weight = layers_18_self_attn_k_proj_weight_to_fp16, x = obj_73_cast_fp16)[name = string("key_37_cast_fp16")];
            string value_37_pad_type_0 = const()[name = string("value_37_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_37_strides_0 = const()[name = string("value_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_37_pad_0 = const()[name = string("value_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_37_dilations_0 = const()[name = string("value_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_37_groups_0 = const()[name = string("value_37_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_18_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(729584832)))];
            tensor<fp16, [1280]> layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(732861696)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_37_cast_fp16 = conv(bias = layers_18_self_attn_v_proj_bias_to_fp16, dilations = value_37_dilations_0, groups = value_37_groups_0, pad = value_37_pad_0, pad_type = value_37_pad_type_0, strides = value_37_strides_0, weight = layers_18_self_attn_v_proj_weight_to_fp16, x = obj_73_cast_fp16)[name = string("value_37_cast_fp16")];
            tensor<int32, [4]> var_2364 = const()[name = string("op_2364"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_37_cast_fp16 = reshape(shape = var_2364, x = query_37_cast_fp16)[name = string("mh_q_37_cast_fp16")];
            fp16 var_2366_to_fp16 = const()[name = string("op_2366_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2367_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = var_2366_to_fp16)[name = string("op_2367_cast_fp16")];
            tensor<int32, [4]> var_2368 = const()[name = string("op_2368"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2369_cast_fp16 = reshape(shape = var_2368, x = key_37_cast_fp16)[name = string("op_2369_cast_fp16")];
            bool mh_w_37_transpose_x_0 = const()[name = string("mh_w_37_transpose_x_0"), val = bool(true)];
            bool mh_w_37_transpose_y_0 = const()[name = string("mh_w_37_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_2367_cast_fp16, y = var_2369_cast_fp16)[name = string("mh_w_37_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2372_cast_fp16 = softmax(axis = var_2310, x = mh_w_37_cast_fp16)[name = string("op_2372_cast_fp16")];
            tensor<int32, [4]> var_2373 = const()[name = string("op_2373"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2374_cast_fp16 = reshape(shape = var_2373, x = value_37_cast_fp16)[name = string("op_2374_cast_fp16")];
            bool attn_37_transpose_x_0 = const()[name = string("attn_37_transpose_x_0"), val = bool(false)];
            bool attn_37_transpose_y_0 = const()[name = string("attn_37_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2374_cast_fp16, y = var_2372_cast_fp16)[name = string("attn_37_cast_fp16")];
            tensor<int32, [4]> var_2377 = const()[name = string("op_2377"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_145_cast_fp16 = reshape(shape = var_2377, x = attn_37_cast_fp16)[name = string("input_145_cast_fp16")];
            string obj_75_pad_type_0 = const()[name = string("obj_75_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_75_strides_0 = const()[name = string("obj_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_75_pad_0 = const()[name = string("obj_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_75_dilations_0 = const()[name = string("obj_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_75_groups_0 = const()[name = string("obj_75_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_18_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(732864320)))];
            tensor<fp16, [1280]> layers_18_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_18_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(736141184)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_75_cast_fp16 = conv(bias = layers_18_self_attn_o_proj_bias_to_fp16, dilations = obj_75_dilations_0, groups = obj_75_groups_0, pad = obj_75_pad_0, pad_type = obj_75_pad_type_0, strides = obj_75_strides_0, weight = layers_18_self_attn_o_proj_weight_to_fp16, x = input_145_cast_fp16)[name = string("obj_75_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_75_cast_fp16 = add(x = inputs_73_cast_fp16, y = obj_75_cast_fp16)[name = string("inputs_75_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2395_to_fp16 = const()[name = string("op_2395_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_2395_to_fp16, x = inputs_75_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<fp16, [1280]> input_147_gamma_0_to_fp16 = const()[name = string("input_147_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(736143808)))];
            tensor<fp16, [1280]> input_147_beta_0_to_fp16 = const()[name = string("input_147_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(736146432)))];
            fp16 input_147_epsilon_0_to_fp16 = const()[name = string("input_147_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_147_cast_fp16 = batch_norm(beta = input_147_beta_0_to_fp16, epsilon = input_147_epsilon_0_to_fp16, gamma = input_147_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_75_cast_fp16)[name = string("input_147_cast_fp16")];
            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_18_fc1_weight_to_fp16 = const()[name = string("layers_18_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(736149056)))];
            tensor<fp16, [5120]> layers_18_fc1_bias_to_fp16 = const()[name = string("layers_18_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(749256320)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_149_cast_fp16 = conv(bias = layers_18_fc1_bias_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = layers_18_fc1_weight_to_fp16, x = input_147_cast_fp16)[name = string("input_149_cast_fp16")];
            string input_151_mode_0 = const()[name = string("input_151_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_151_cast_fp16 = gelu(mode = input_151_mode_0, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")];
            string hidden_states_41_pad_type_0 = const()[name = string("hidden_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_41_strides_0 = const()[name = string("hidden_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_41_pad_0 = const()[name = string("hidden_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_41_dilations_0 = const()[name = string("hidden_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_41_groups_0 = const()[name = string("hidden_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_18_fc2_weight_to_fp16 = const()[name = string("layers_18_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(749266624)))];
            tensor<fp16, [1280]> layers_18_fc2_bias_to_fp16 = const()[name = string("layers_18_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762373888)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_41_cast_fp16 = conv(bias = layers_18_fc2_bias_to_fp16, dilations = hidden_states_41_dilations_0, groups = hidden_states_41_groups_0, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = hidden_states_41_strides_0, weight = layers_18_fc2_weight_to_fp16, x = input_151_cast_fp16)[name = string("hidden_states_41_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_77_cast_fp16 = add(x = inputs_75_cast_fp16, y = hidden_states_41_cast_fp16)[name = string("inputs_77_cast_fp16")];
            int32 var_2428 = const()[name = string("op_2428"), val = int32(3)];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2447_to_fp16 = const()[name = string("op_2447_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_2447_to_fp16, x = inputs_77_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<fp16, [1280]> obj_77_gamma_0_to_fp16 = const()[name = string("obj_77_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762376512)))];
            tensor<fp16, [1280]> obj_77_beta_0_to_fp16 = const()[name = string("obj_77_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762379136)))];
            fp16 obj_77_epsilon_0_to_fp16 = const()[name = string("obj_77_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_77_cast_fp16 = batch_norm(beta = obj_77_beta_0_to_fp16, epsilon = obj_77_epsilon_0_to_fp16, gamma = obj_77_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_77_cast_fp16)[name = string("obj_77_cast_fp16")];
            string query_39_pad_type_0 = const()[name = string("query_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_39_strides_0 = const()[name = string("query_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_39_pad_0 = const()[name = string("query_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_39_dilations_0 = const()[name = string("query_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_39_groups_0 = const()[name = string("query_39_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762381760)))];
            tensor<fp16, [1280]> layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(765658624)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_39_cast_fp16 = conv(bias = layers_19_self_attn_q_proj_bias_to_fp16, dilations = query_39_dilations_0, groups = query_39_groups_0, pad = query_39_pad_0, pad_type = query_39_pad_type_0, strides = query_39_strides_0, weight = layers_19_self_attn_q_proj_weight_to_fp16, x = obj_77_cast_fp16)[name = string("query_39_cast_fp16")];
            string key_39_pad_type_0 = const()[name = string("key_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_39_strides_0 = const()[name = string("key_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_39_pad_0 = const()[name = string("key_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_39_dilations_0 = const()[name = string("key_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_39_groups_0 = const()[name = string("key_39_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(765661248)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_39_cast_fp16 = conv(dilations = key_39_dilations_0, groups = key_39_groups_0, pad = key_39_pad_0, pad_type = key_39_pad_type_0, strides = key_39_strides_0, weight = layers_19_self_attn_k_proj_weight_to_fp16, x = obj_77_cast_fp16)[name = string("key_39_cast_fp16")];
            string value_39_pad_type_0 = const()[name = string("value_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_39_strides_0 = const()[name = string("value_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_39_pad_0 = const()[name = string("value_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_39_dilations_0 = const()[name = string("value_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_39_groups_0 = const()[name = string("value_39_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(768938112)))];
            tensor<fp16, [1280]> layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(772214976)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_39_cast_fp16 = conv(bias = layers_19_self_attn_v_proj_bias_to_fp16, dilations = value_39_dilations_0, groups = value_39_groups_0, pad = value_39_pad_0, pad_type = value_39_pad_type_0, strides = value_39_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = obj_77_cast_fp16)[name = string("value_39_cast_fp16")];
            tensor<int32, [4]> var_2482 = const()[name = string("op_2482"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_39_cast_fp16 = reshape(shape = var_2482, x = query_39_cast_fp16)[name = string("mh_q_39_cast_fp16")];
            fp16 var_2484_to_fp16 = const()[name = string("op_2484_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2485_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_2484_to_fp16)[name = string("op_2485_cast_fp16")];
            tensor<int32, [4]> var_2486 = const()[name = string("op_2486"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2487_cast_fp16 = reshape(shape = var_2486, x = key_39_cast_fp16)[name = string("op_2487_cast_fp16")];
            bool mh_w_39_transpose_x_0 = const()[name = string("mh_w_39_transpose_x_0"), val = bool(true)];
            bool mh_w_39_transpose_y_0 = const()[name = string("mh_w_39_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_39_cast_fp16 = matmul(transpose_x = mh_w_39_transpose_x_0, transpose_y = mh_w_39_transpose_y_0, x = var_2485_cast_fp16, y = var_2487_cast_fp16)[name = string("mh_w_39_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2490_cast_fp16 = softmax(axis = var_2428, x = mh_w_39_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [4]> var_2491 = const()[name = string("op_2491"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2492_cast_fp16 = reshape(shape = var_2491, x = value_39_cast_fp16)[name = string("op_2492_cast_fp16")];
            bool attn_39_transpose_x_0 = const()[name = string("attn_39_transpose_x_0"), val = bool(false)];
            bool attn_39_transpose_y_0 = const()[name = string("attn_39_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2492_cast_fp16, y = var_2490_cast_fp16)[name = string("attn_39_cast_fp16")];
            tensor<int32, [4]> var_2495 = const()[name = string("op_2495"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_153_cast_fp16 = reshape(shape = var_2495, x = attn_39_cast_fp16)[name = string("input_153_cast_fp16")];
            string obj_79_pad_type_0 = const()[name = string("obj_79_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_79_strides_0 = const()[name = string("obj_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_79_pad_0 = const()[name = string("obj_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_79_dilations_0 = const()[name = string("obj_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_79_groups_0 = const()[name = string("obj_79_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(772217600)))];
            tensor<fp16, [1280]> layers_19_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775494464)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_79_cast_fp16 = conv(bias = layers_19_self_attn_o_proj_bias_to_fp16, dilations = obj_79_dilations_0, groups = obj_79_groups_0, pad = obj_79_pad_0, pad_type = obj_79_pad_type_0, strides = obj_79_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = input_153_cast_fp16)[name = string("obj_79_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_79_cast_fp16 = add(x = inputs_77_cast_fp16, y = obj_79_cast_fp16)[name = string("inputs_79_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2513_to_fp16 = const()[name = string("op_2513_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_2513_to_fp16, x = inputs_79_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<fp16, [1280]> input_155_gamma_0_to_fp16 = const()[name = string("input_155_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775497088)))];
            tensor<fp16, [1280]> input_155_beta_0_to_fp16 = const()[name = string("input_155_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775499712)))];
            fp16 input_155_epsilon_0_to_fp16 = const()[name = string("input_155_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_155_cast_fp16 = batch_norm(beta = input_155_beta_0_to_fp16, epsilon = input_155_epsilon_0_to_fp16, gamma = input_155_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_79_cast_fp16)[name = string("input_155_cast_fp16")];
            string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_19_fc1_weight_to_fp16 = const()[name = string("layers_19_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775502336)))];
            tensor<fp16, [5120]> layers_19_fc1_bias_to_fp16 = const()[name = string("layers_19_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(788609600)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_157_cast_fp16 = conv(bias = layers_19_fc1_bias_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = layers_19_fc1_weight_to_fp16, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")];
            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_159_cast_fp16 = gelu(mode = input_159_mode_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_19_fc2_weight_to_fp16 = const()[name = string("layers_19_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(788619904)))];
            tensor<fp16, [1280]> layers_19_fc2_bias_to_fp16 = const()[name = string("layers_19_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801727168)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_43_cast_fp16 = conv(bias = layers_19_fc2_bias_to_fp16, dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_19_fc2_weight_to_fp16, x = input_159_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_81_cast_fp16 = add(x = inputs_79_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("inputs_81_cast_fp16")];
            int32 var_2546 = const()[name = string("op_2546"), val = int32(3)];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2565_to_fp16 = const()[name = string("op_2565_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_2565_to_fp16, x = inputs_81_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<fp16, [1280]> obj_81_gamma_0_to_fp16 = const()[name = string("obj_81_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801729792)))];
            tensor<fp16, [1280]> obj_81_beta_0_to_fp16 = const()[name = string("obj_81_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801732416)))];
            fp16 obj_81_epsilon_0_to_fp16 = const()[name = string("obj_81_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_81_cast_fp16 = batch_norm(beta = obj_81_beta_0_to_fp16, epsilon = obj_81_epsilon_0_to_fp16, gamma = obj_81_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_81_cast_fp16)[name = string("obj_81_cast_fp16")];
            string query_41_pad_type_0 = const()[name = string("query_41_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_41_strides_0 = const()[name = string("query_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_41_pad_0 = const()[name = string("query_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_41_dilations_0 = const()[name = string("query_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_41_groups_0 = const()[name = string("query_41_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801735040)))];
            tensor<fp16, [1280]> layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(805011904)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_41_cast_fp16 = conv(bias = layers_20_self_attn_q_proj_bias_to_fp16, dilations = query_41_dilations_0, groups = query_41_groups_0, pad = query_41_pad_0, pad_type = query_41_pad_type_0, strides = query_41_strides_0, weight = layers_20_self_attn_q_proj_weight_to_fp16, x = obj_81_cast_fp16)[name = string("query_41_cast_fp16")];
            string key_41_pad_type_0 = const()[name = string("key_41_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_41_strides_0 = const()[name = string("key_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_41_pad_0 = const()[name = string("key_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_41_dilations_0 = const()[name = string("key_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_41_groups_0 = const()[name = string("key_41_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(805014528)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_41_cast_fp16 = conv(dilations = key_41_dilations_0, groups = key_41_groups_0, pad = key_41_pad_0, pad_type = key_41_pad_type_0, strides = key_41_strides_0, weight = layers_20_self_attn_k_proj_weight_to_fp16, x = obj_81_cast_fp16)[name = string("key_41_cast_fp16")];
            string value_41_pad_type_0 = const()[name = string("value_41_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_41_strides_0 = const()[name = string("value_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_41_pad_0 = const()[name = string("value_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_41_dilations_0 = const()[name = string("value_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_41_groups_0 = const()[name = string("value_41_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808291392)))];
            tensor<fp16, [1280]> layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(811568256)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_41_cast_fp16 = conv(bias = layers_20_self_attn_v_proj_bias_to_fp16, dilations = value_41_dilations_0, groups = value_41_groups_0, pad = value_41_pad_0, pad_type = value_41_pad_type_0, strides = value_41_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = obj_81_cast_fp16)[name = string("value_41_cast_fp16")];
            tensor<int32, [4]> var_2600 = const()[name = string("op_2600"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_41_cast_fp16 = reshape(shape = var_2600, x = query_41_cast_fp16)[name = string("mh_q_41_cast_fp16")];
            fp16 var_2602_to_fp16 = const()[name = string("op_2602_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2603_cast_fp16 = mul(x = mh_q_41_cast_fp16, y = var_2602_to_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [4]> var_2604 = const()[name = string("op_2604"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2605_cast_fp16 = reshape(shape = var_2604, x = key_41_cast_fp16)[name = string("op_2605_cast_fp16")];
            bool mh_w_41_transpose_x_0 = const()[name = string("mh_w_41_transpose_x_0"), val = bool(true)];
            bool mh_w_41_transpose_y_0 = const()[name = string("mh_w_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_41_cast_fp16 = matmul(transpose_x = mh_w_41_transpose_x_0, transpose_y = mh_w_41_transpose_y_0, x = var_2603_cast_fp16, y = var_2605_cast_fp16)[name = string("mh_w_41_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2608_cast_fp16 = softmax(axis = var_2546, x = mh_w_41_cast_fp16)[name = string("op_2608_cast_fp16")];
            tensor<int32, [4]> var_2609 = const()[name = string("op_2609"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2610_cast_fp16 = reshape(shape = var_2609, x = value_41_cast_fp16)[name = string("op_2610_cast_fp16")];
            bool attn_41_transpose_x_0 = const()[name = string("attn_41_transpose_x_0"), val = bool(false)];
            bool attn_41_transpose_y_0 = const()[name = string("attn_41_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2610_cast_fp16, y = var_2608_cast_fp16)[name = string("attn_41_cast_fp16")];
            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_161_cast_fp16 = reshape(shape = var_2613, x = attn_41_cast_fp16)[name = string("input_161_cast_fp16")];
            string obj_83_pad_type_0 = const()[name = string("obj_83_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_83_strides_0 = const()[name = string("obj_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_83_pad_0 = const()[name = string("obj_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_83_dilations_0 = const()[name = string("obj_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_83_groups_0 = const()[name = string("obj_83_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(811570880)))];
            tensor<fp16, [1280]> layers_20_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_20_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(814847744)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_83_cast_fp16 = conv(bias = layers_20_self_attn_o_proj_bias_to_fp16, dilations = obj_83_dilations_0, groups = obj_83_groups_0, pad = obj_83_pad_0, pad_type = obj_83_pad_type_0, strides = obj_83_strides_0, weight = layers_20_self_attn_o_proj_weight_to_fp16, x = input_161_cast_fp16)[name = string("obj_83_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_83_cast_fp16 = add(x = inputs_81_cast_fp16, y = obj_83_cast_fp16)[name = string("inputs_83_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2631_to_fp16 = const()[name = string("op_2631_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_2631_to_fp16, x = inputs_83_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<fp16, [1280]> input_163_gamma_0_to_fp16 = const()[name = string("input_163_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(814850368)))];
            tensor<fp16, [1280]> input_163_beta_0_to_fp16 = const()[name = string("input_163_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(814852992)))];
            fp16 input_163_epsilon_0_to_fp16 = const()[name = string("input_163_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_163_cast_fp16 = batch_norm(beta = input_163_beta_0_to_fp16, epsilon = input_163_epsilon_0_to_fp16, gamma = input_163_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_83_cast_fp16)[name = string("input_163_cast_fp16")];
            string input_165_pad_type_0 = const()[name = string("input_165_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_165_strides_0 = const()[name = string("input_165_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_165_pad_0 = const()[name = string("input_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_165_dilations_0 = const()[name = string("input_165_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_165_groups_0 = const()[name = string("input_165_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_20_fc1_weight_to_fp16 = const()[name = string("layers_20_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(814855616)))];
            tensor<fp16, [5120]> layers_20_fc1_bias_to_fp16 = const()[name = string("layers_20_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(827962880)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_165_cast_fp16 = conv(bias = layers_20_fc1_bias_to_fp16, dilations = input_165_dilations_0, groups = input_165_groups_0, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = input_165_strides_0, weight = layers_20_fc1_weight_to_fp16, x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
            string input_167_mode_0 = const()[name = string("input_167_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_167_cast_fp16 = gelu(mode = input_167_mode_0, x = input_165_cast_fp16)[name = string("input_167_cast_fp16")];
            string hidden_states_45_pad_type_0 = const()[name = string("hidden_states_45_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_45_strides_0 = const()[name = string("hidden_states_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = string("hidden_states_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_45_dilations_0 = const()[name = string("hidden_states_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_45_groups_0 = const()[name = string("hidden_states_45_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_20_fc2_weight_to_fp16 = const()[name = string("layers_20_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(827973184)))];
            tensor<fp16, [1280]> layers_20_fc2_bias_to_fp16 = const()[name = string("layers_20_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841080448)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_45_cast_fp16 = conv(bias = layers_20_fc2_bias_to_fp16, dilations = hidden_states_45_dilations_0, groups = hidden_states_45_groups_0, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = hidden_states_45_strides_0, weight = layers_20_fc2_weight_to_fp16, x = input_167_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_85_cast_fp16 = add(x = inputs_83_cast_fp16, y = hidden_states_45_cast_fp16)[name = string("inputs_85_cast_fp16")];
            int32 var_2664 = const()[name = string("op_2664"), val = int32(3)];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2683_to_fp16 = const()[name = string("op_2683_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_2683_to_fp16, x = inputs_85_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<fp16, [1280]> obj_85_gamma_0_to_fp16 = const()[name = string("obj_85_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841083072)))];
            tensor<fp16, [1280]> obj_85_beta_0_to_fp16 = const()[name = string("obj_85_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841085696)))];
            fp16 obj_85_epsilon_0_to_fp16 = const()[name = string("obj_85_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_85_cast_fp16 = batch_norm(beta = obj_85_beta_0_to_fp16, epsilon = obj_85_epsilon_0_to_fp16, gamma = obj_85_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_85_cast_fp16)[name = string("obj_85_cast_fp16")];
            string query_43_pad_type_0 = const()[name = string("query_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_43_strides_0 = const()[name = string("query_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_43_pad_0 = const()[name = string("query_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_43_dilations_0 = const()[name = string("query_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_43_groups_0 = const()[name = string("query_43_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841088320)))];
            tensor<fp16, [1280]> layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(844365184)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_43_cast_fp16 = conv(bias = layers_21_self_attn_q_proj_bias_to_fp16, dilations = query_43_dilations_0, groups = query_43_groups_0, pad = query_43_pad_0, pad_type = query_43_pad_type_0, strides = query_43_strides_0, weight = layers_21_self_attn_q_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = string("query_43_cast_fp16")];
            string key_43_pad_type_0 = const()[name = string("key_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_43_strides_0 = const()[name = string("key_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_43_pad_0 = const()[name = string("key_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_43_dilations_0 = const()[name = string("key_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_43_groups_0 = const()[name = string("key_43_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(844367808)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_43_cast_fp16 = conv(dilations = key_43_dilations_0, groups = key_43_groups_0, pad = key_43_pad_0, pad_type = key_43_pad_type_0, strides = key_43_strides_0, weight = layers_21_self_attn_k_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = string("key_43_cast_fp16")];
            string value_43_pad_type_0 = const()[name = string("value_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_43_strides_0 = const()[name = string("value_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_43_pad_0 = const()[name = string("value_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_43_dilations_0 = const()[name = string("value_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_43_groups_0 = const()[name = string("value_43_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(847644672)))];
            tensor<fp16, [1280]> layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(850921536)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_43_cast_fp16 = conv(bias = layers_21_self_attn_v_proj_bias_to_fp16, dilations = value_43_dilations_0, groups = value_43_groups_0, pad = value_43_pad_0, pad_type = value_43_pad_type_0, strides = value_43_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = string("value_43_cast_fp16")];
            tensor<int32, [4]> var_2718 = const()[name = string("op_2718"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_43_cast_fp16 = reshape(shape = var_2718, x = query_43_cast_fp16)[name = string("mh_q_43_cast_fp16")];
            fp16 var_2720_to_fp16 = const()[name = string("op_2720_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2721_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = var_2720_to_fp16)[name = string("op_2721_cast_fp16")];
            tensor<int32, [4]> var_2722 = const()[name = string("op_2722"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2723_cast_fp16 = reshape(shape = var_2722, x = key_43_cast_fp16)[name = string("op_2723_cast_fp16")];
            bool mh_w_43_transpose_x_0 = const()[name = string("mh_w_43_transpose_x_0"), val = bool(true)];
            bool mh_w_43_transpose_y_0 = const()[name = string("mh_w_43_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_43_cast_fp16 = matmul(transpose_x = mh_w_43_transpose_x_0, transpose_y = mh_w_43_transpose_y_0, x = var_2721_cast_fp16, y = var_2723_cast_fp16)[name = string("mh_w_43_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2726_cast_fp16 = softmax(axis = var_2664, x = mh_w_43_cast_fp16)[name = string("op_2726_cast_fp16")];
            tensor<int32, [4]> var_2727 = const()[name = string("op_2727"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2728_cast_fp16 = reshape(shape = var_2727, x = value_43_cast_fp16)[name = string("op_2728_cast_fp16")];
            bool attn_43_transpose_x_0 = const()[name = string("attn_43_transpose_x_0"), val = bool(false)];
            bool attn_43_transpose_y_0 = const()[name = string("attn_43_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2728_cast_fp16, y = var_2726_cast_fp16)[name = string("attn_43_cast_fp16")];
            tensor<int32, [4]> var_2731 = const()[name = string("op_2731"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_169_cast_fp16 = reshape(shape = var_2731, x = attn_43_cast_fp16)[name = string("input_169_cast_fp16")];
            string obj_87_pad_type_0 = const()[name = string("obj_87_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_87_strides_0 = const()[name = string("obj_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_87_pad_0 = const()[name = string("obj_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_87_dilations_0 = const()[name = string("obj_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_87_groups_0 = const()[name = string("obj_87_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(850924160)))];
            tensor<fp16, [1280]> layers_21_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_21_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854201024)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_87_cast_fp16 = conv(bias = layers_21_self_attn_o_proj_bias_to_fp16, dilations = obj_87_dilations_0, groups = obj_87_groups_0, pad = obj_87_pad_0, pad_type = obj_87_pad_type_0, strides = obj_87_strides_0, weight = layers_21_self_attn_o_proj_weight_to_fp16, x = input_169_cast_fp16)[name = string("obj_87_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_87_cast_fp16 = add(x = inputs_85_cast_fp16, y = obj_87_cast_fp16)[name = string("inputs_87_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2749_to_fp16 = const()[name = string("op_2749_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_2749_to_fp16, x = inputs_87_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<fp16, [1280]> input_171_gamma_0_to_fp16 = const()[name = string("input_171_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854203648)))];
            tensor<fp16, [1280]> input_171_beta_0_to_fp16 = const()[name = string("input_171_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854206272)))];
            fp16 input_171_epsilon_0_to_fp16 = const()[name = string("input_171_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_171_cast_fp16 = batch_norm(beta = input_171_beta_0_to_fp16, epsilon = input_171_epsilon_0_to_fp16, gamma = input_171_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_87_cast_fp16)[name = string("input_171_cast_fp16")];
            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_21_fc1_weight_to_fp16 = const()[name = string("layers_21_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854208896)))];
            tensor<fp16, [5120]> layers_21_fc1_bias_to_fp16 = const()[name = string("layers_21_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867316160)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_173_cast_fp16 = conv(bias = layers_21_fc1_bias_to_fp16, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = layers_21_fc1_weight_to_fp16, x = input_171_cast_fp16)[name = string("input_173_cast_fp16")];
            string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_21_fc2_weight_to_fp16 = const()[name = string("layers_21_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867326464)))];
            tensor<fp16, [1280]> layers_21_fc2_bias_to_fp16 = const()[name = string("layers_21_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(880433728)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_47_cast_fp16 = conv(bias = layers_21_fc2_bias_to_fp16, dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_21_fc2_weight_to_fp16, x = input_175_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_89_cast_fp16 = add(x = inputs_87_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("inputs_89_cast_fp16")];
            int32 var_2782 = const()[name = string("op_2782"), val = int32(3)];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2801_to_fp16 = const()[name = string("op_2801_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_2801_to_fp16, x = inputs_89_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<fp16, [1280]> obj_89_gamma_0_to_fp16 = const()[name = string("obj_89_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(880436352)))];
            tensor<fp16, [1280]> obj_89_beta_0_to_fp16 = const()[name = string("obj_89_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(880438976)))];
            fp16 obj_89_epsilon_0_to_fp16 = const()[name = string("obj_89_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_89_cast_fp16 = batch_norm(beta = obj_89_beta_0_to_fp16, epsilon = obj_89_epsilon_0_to_fp16, gamma = obj_89_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_89_cast_fp16)[name = string("obj_89_cast_fp16")];
            string query_45_pad_type_0 = const()[name = string("query_45_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_45_strides_0 = const()[name = string("query_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_45_pad_0 = const()[name = string("query_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_45_dilations_0 = const()[name = string("query_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_45_groups_0 = const()[name = string("query_45_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(880441600)))];
            tensor<fp16, [1280]> layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(883718464)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_45_cast_fp16 = conv(bias = layers_22_self_attn_q_proj_bias_to_fp16, dilations = query_45_dilations_0, groups = query_45_groups_0, pad = query_45_pad_0, pad_type = query_45_pad_type_0, strides = query_45_strides_0, weight = layers_22_self_attn_q_proj_weight_to_fp16, x = obj_89_cast_fp16)[name = string("query_45_cast_fp16")];
            string key_45_pad_type_0 = const()[name = string("key_45_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_45_strides_0 = const()[name = string("key_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_45_pad_0 = const()[name = string("key_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_45_dilations_0 = const()[name = string("key_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_45_groups_0 = const()[name = string("key_45_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(883721088)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_45_cast_fp16 = conv(dilations = key_45_dilations_0, groups = key_45_groups_0, pad = key_45_pad_0, pad_type = key_45_pad_type_0, strides = key_45_strides_0, weight = layers_22_self_attn_k_proj_weight_to_fp16, x = obj_89_cast_fp16)[name = string("key_45_cast_fp16")];
            string value_45_pad_type_0 = const()[name = string("value_45_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_45_strides_0 = const()[name = string("value_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_45_pad_0 = const()[name = string("value_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_45_dilations_0 = const()[name = string("value_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_45_groups_0 = const()[name = string("value_45_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(886997952)))];
            tensor<fp16, [1280]> layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890274816)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_45_cast_fp16 = conv(bias = layers_22_self_attn_v_proj_bias_to_fp16, dilations = value_45_dilations_0, groups = value_45_groups_0, pad = value_45_pad_0, pad_type = value_45_pad_type_0, strides = value_45_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = obj_89_cast_fp16)[name = string("value_45_cast_fp16")];
            tensor<int32, [4]> var_2836 = const()[name = string("op_2836"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_45_cast_fp16 = reshape(shape = var_2836, x = query_45_cast_fp16)[name = string("mh_q_45_cast_fp16")];
            fp16 var_2838_to_fp16 = const()[name = string("op_2838_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2839_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_2838_to_fp16)[name = string("op_2839_cast_fp16")];
            tensor<int32, [4]> var_2840 = const()[name = string("op_2840"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2841_cast_fp16 = reshape(shape = var_2840, x = key_45_cast_fp16)[name = string("op_2841_cast_fp16")];
            bool mh_w_45_transpose_x_0 = const()[name = string("mh_w_45_transpose_x_0"), val = bool(true)];
            bool mh_w_45_transpose_y_0 = const()[name = string("mh_w_45_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_45_cast_fp16 = matmul(transpose_x = mh_w_45_transpose_x_0, transpose_y = mh_w_45_transpose_y_0, x = var_2839_cast_fp16, y = var_2841_cast_fp16)[name = string("mh_w_45_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2844_cast_fp16 = softmax(axis = var_2782, x = mh_w_45_cast_fp16)[name = string("op_2844_cast_fp16")];
            tensor<int32, [4]> var_2845 = const()[name = string("op_2845"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2846_cast_fp16 = reshape(shape = var_2845, x = value_45_cast_fp16)[name = string("op_2846_cast_fp16")];
            bool attn_45_transpose_x_0 = const()[name = string("attn_45_transpose_x_0"), val = bool(false)];
            bool attn_45_transpose_y_0 = const()[name = string("attn_45_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2846_cast_fp16, y = var_2844_cast_fp16)[name = string("attn_45_cast_fp16")];
            tensor<int32, [4]> var_2849 = const()[name = string("op_2849"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_177_cast_fp16 = reshape(shape = var_2849, x = attn_45_cast_fp16)[name = string("input_177_cast_fp16")];
            string obj_91_pad_type_0 = const()[name = string("obj_91_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_91_strides_0 = const()[name = string("obj_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_91_pad_0 = const()[name = string("obj_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_91_dilations_0 = const()[name = string("obj_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_91_groups_0 = const()[name = string("obj_91_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890277440)))];
            tensor<fp16, [1280]> layers_22_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(893554304)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_91_cast_fp16 = conv(bias = layers_22_self_attn_o_proj_bias_to_fp16, dilations = obj_91_dilations_0, groups = obj_91_groups_0, pad = obj_91_pad_0, pad_type = obj_91_pad_type_0, strides = obj_91_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = input_177_cast_fp16)[name = string("obj_91_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_91_cast_fp16 = add(x = inputs_89_cast_fp16, y = obj_91_cast_fp16)[name = string("inputs_91_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2867_to_fp16 = const()[name = string("op_2867_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_2867_to_fp16, x = inputs_91_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<fp16, [1280]> input_179_gamma_0_to_fp16 = const()[name = string("input_179_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(893556928)))];
            tensor<fp16, [1280]> input_179_beta_0_to_fp16 = const()[name = string("input_179_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(893559552)))];
            fp16 input_179_epsilon_0_to_fp16 = const()[name = string("input_179_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_179_cast_fp16 = batch_norm(beta = input_179_beta_0_to_fp16, epsilon = input_179_epsilon_0_to_fp16, gamma = input_179_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_91_cast_fp16)[name = string("input_179_cast_fp16")];
            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_22_fc1_weight_to_fp16 = const()[name = string("layers_22_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(893562176)))];
            tensor<fp16, [5120]> layers_22_fc1_bias_to_fp16 = const()[name = string("layers_22_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906669440)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_181_cast_fp16 = conv(bias = layers_22_fc1_bias_to_fp16, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = layers_22_fc1_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")];
            string input_183_mode_0 = const()[name = string("input_183_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_183_cast_fp16 = gelu(mode = input_183_mode_0, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
            string hidden_states_49_pad_type_0 = const()[name = string("hidden_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_49_strides_0 = const()[name = string("hidden_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_49_pad_0 = const()[name = string("hidden_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_49_dilations_0 = const()[name = string("hidden_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_49_groups_0 = const()[name = string("hidden_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_22_fc2_weight_to_fp16 = const()[name = string("layers_22_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906679744)))];
            tensor<fp16, [1280]> layers_22_fc2_bias_to_fp16 = const()[name = string("layers_22_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(919787008)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_49_cast_fp16 = conv(bias = layers_22_fc2_bias_to_fp16, dilations = hidden_states_49_dilations_0, groups = hidden_states_49_groups_0, pad = hidden_states_49_pad_0, pad_type = hidden_states_49_pad_type_0, strides = hidden_states_49_strides_0, weight = layers_22_fc2_weight_to_fp16, x = input_183_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_93_cast_fp16 = add(x = inputs_91_cast_fp16, y = hidden_states_49_cast_fp16)[name = string("inputs_93_cast_fp16")];
            int32 var_2900 = const()[name = string("op_2900"), val = int32(3)];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2919_to_fp16 = const()[name = string("op_2919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_2919_to_fp16, x = inputs_93_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<fp16, [1280]> obj_93_gamma_0_to_fp16 = const()[name = string("obj_93_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(919789632)))];
            tensor<fp16, [1280]> obj_93_beta_0_to_fp16 = const()[name = string("obj_93_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(919792256)))];
            fp16 obj_93_epsilon_0_to_fp16 = const()[name = string("obj_93_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_93_cast_fp16 = batch_norm(beta = obj_93_beta_0_to_fp16, epsilon = obj_93_epsilon_0_to_fp16, gamma = obj_93_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_93_cast_fp16)[name = string("obj_93_cast_fp16")];
            string query_47_pad_type_0 = const()[name = string("query_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_47_strides_0 = const()[name = string("query_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_47_pad_0 = const()[name = string("query_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_47_dilations_0 = const()[name = string("query_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_47_groups_0 = const()[name = string("query_47_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(919794880)))];
            tensor<fp16, [1280]> layers_23_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_23_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923071744)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_47_cast_fp16 = conv(bias = layers_23_self_attn_q_proj_bias_to_fp16, dilations = query_47_dilations_0, groups = query_47_groups_0, pad = query_47_pad_0, pad_type = query_47_pad_type_0, strides = query_47_strides_0, weight = layers_23_self_attn_q_proj_weight_to_fp16, x = obj_93_cast_fp16)[name = string("query_47_cast_fp16")];
            string key_47_pad_type_0 = const()[name = string("key_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_47_strides_0 = const()[name = string("key_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_47_pad_0 = const()[name = string("key_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_47_dilations_0 = const()[name = string("key_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_47_groups_0 = const()[name = string("key_47_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923074368)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_47_cast_fp16 = conv(dilations = key_47_dilations_0, groups = key_47_groups_0, pad = key_47_pad_0, pad_type = key_47_pad_type_0, strides = key_47_strides_0, weight = layers_23_self_attn_k_proj_weight_to_fp16, x = obj_93_cast_fp16)[name = string("key_47_cast_fp16")];
            string value_47_pad_type_0 = const()[name = string("value_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_47_strides_0 = const()[name = string("value_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_47_pad_0 = const()[name = string("value_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_47_dilations_0 = const()[name = string("value_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_47_groups_0 = const()[name = string("value_47_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(926351232)))];
            tensor<fp16, [1280]> layers_23_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929628096)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_47_cast_fp16 = conv(bias = layers_23_self_attn_v_proj_bias_to_fp16, dilations = value_47_dilations_0, groups = value_47_groups_0, pad = value_47_pad_0, pad_type = value_47_pad_type_0, strides = value_47_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = obj_93_cast_fp16)[name = string("value_47_cast_fp16")];
            tensor<int32, [4]> var_2954 = const()[name = string("op_2954"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_47_cast_fp16 = reshape(shape = var_2954, x = query_47_cast_fp16)[name = string("mh_q_47_cast_fp16")];
            fp16 var_2956_to_fp16 = const()[name = string("op_2956_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_2957_cast_fp16 = mul(x = mh_q_47_cast_fp16, y = var_2956_to_fp16)[name = string("op_2957_cast_fp16")];
            tensor<int32, [4]> var_2958 = const()[name = string("op_2958"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2959_cast_fp16 = reshape(shape = var_2958, x = key_47_cast_fp16)[name = string("op_2959_cast_fp16")];
            bool mh_w_47_transpose_x_0 = const()[name = string("mh_w_47_transpose_x_0"), val = bool(true)];
            bool mh_w_47_transpose_y_0 = const()[name = string("mh_w_47_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_47_cast_fp16 = matmul(transpose_x = mh_w_47_transpose_x_0, transpose_y = mh_w_47_transpose_y_0, x = var_2957_cast_fp16, y = var_2959_cast_fp16)[name = string("mh_w_47_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_2962_cast_fp16 = softmax(axis = var_2900, x = mh_w_47_cast_fp16)[name = string("op_2962_cast_fp16")];
            tensor<int32, [4]> var_2963 = const()[name = string("op_2963"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_2964_cast_fp16 = reshape(shape = var_2963, x = value_47_cast_fp16)[name = string("op_2964_cast_fp16")];
            bool attn_47_transpose_x_0 = const()[name = string("attn_47_transpose_x_0"), val = bool(false)];
            bool attn_47_transpose_y_0 = const()[name = string("attn_47_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2964_cast_fp16, y = var_2962_cast_fp16)[name = string("attn_47_cast_fp16")];
            tensor<int32, [4]> var_2967 = const()[name = string("op_2967"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_185_cast_fp16 = reshape(shape = var_2967, x = attn_47_cast_fp16)[name = string("input_185_cast_fp16")];
            string obj_95_pad_type_0 = const()[name = string("obj_95_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_95_strides_0 = const()[name = string("obj_95_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_95_pad_0 = const()[name = string("obj_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_95_dilations_0 = const()[name = string("obj_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_95_groups_0 = const()[name = string("obj_95_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929630720)))];
            tensor<fp16, [1280]> layers_23_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_23_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932907584)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_95_cast_fp16 = conv(bias = layers_23_self_attn_o_proj_bias_to_fp16, dilations = obj_95_dilations_0, groups = obj_95_groups_0, pad = obj_95_pad_0, pad_type = obj_95_pad_type_0, strides = obj_95_strides_0, weight = layers_23_self_attn_o_proj_weight_to_fp16, x = input_185_cast_fp16)[name = string("obj_95_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_95_cast_fp16 = add(x = inputs_93_cast_fp16, y = obj_95_cast_fp16)[name = string("inputs_95_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2985_to_fp16 = const()[name = string("op_2985_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_2985_to_fp16, x = inputs_95_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<fp16, [1280]> input_187_gamma_0_to_fp16 = const()[name = string("input_187_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932910208)))];
            tensor<fp16, [1280]> input_187_beta_0_to_fp16 = const()[name = string("input_187_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932912832)))];
            fp16 input_187_epsilon_0_to_fp16 = const()[name = string("input_187_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_187_cast_fp16 = batch_norm(beta = input_187_beta_0_to_fp16, epsilon = input_187_epsilon_0_to_fp16, gamma = input_187_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_95_cast_fp16)[name = string("input_187_cast_fp16")];
            string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_23_fc1_weight_to_fp16 = const()[name = string("layers_23_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932915456)))];
            tensor<fp16, [5120]> layers_23_fc1_bias_to_fp16 = const()[name = string("layers_23_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946022720)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_189_cast_fp16 = conv(bias = layers_23_fc1_bias_to_fp16, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = layers_23_fc1_weight_to_fp16, x = input_187_cast_fp16)[name = string("input_189_cast_fp16")];
            string input_191_mode_0 = const()[name = string("input_191_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_191_cast_fp16 = gelu(mode = input_191_mode_0, x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
            string hidden_states_51_pad_type_0 = const()[name = string("hidden_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_51_strides_0 = const()[name = string("hidden_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_51_pad_0 = const()[name = string("hidden_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_51_dilations_0 = const()[name = string("hidden_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_51_groups_0 = const()[name = string("hidden_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_23_fc2_weight_to_fp16 = const()[name = string("layers_23_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946033024)))];
            tensor<fp16, [1280]> layers_23_fc2_bias_to_fp16 = const()[name = string("layers_23_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(959140288)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_51_cast_fp16 = conv(bias = layers_23_fc2_bias_to_fp16, dilations = hidden_states_51_dilations_0, groups = hidden_states_51_groups_0, pad = hidden_states_51_pad_0, pad_type = hidden_states_51_pad_type_0, strides = hidden_states_51_strides_0, weight = layers_23_fc2_weight_to_fp16, x = input_191_cast_fp16)[name = string("hidden_states_51_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_97_cast_fp16 = add(x = inputs_95_cast_fp16, y = hidden_states_51_cast_fp16)[name = string("inputs_97_cast_fp16")];
            int32 var_3018 = const()[name = string("op_3018"), val = int32(3)];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3037_to_fp16 = const()[name = string("op_3037_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_3037_to_fp16, x = inputs_97_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<fp16, [1280]> obj_97_gamma_0_to_fp16 = const()[name = string("obj_97_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(959142912)))];
            tensor<fp16, [1280]> obj_97_beta_0_to_fp16 = const()[name = string("obj_97_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(959145536)))];
            fp16 obj_97_epsilon_0_to_fp16 = const()[name = string("obj_97_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_97_cast_fp16 = batch_norm(beta = obj_97_beta_0_to_fp16, epsilon = obj_97_epsilon_0_to_fp16, gamma = obj_97_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_97_cast_fp16)[name = string("obj_97_cast_fp16")];
            string query_49_pad_type_0 = const()[name = string("query_49_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_49_strides_0 = const()[name = string("query_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_49_pad_0 = const()[name = string("query_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_49_dilations_0 = const()[name = string("query_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_49_groups_0 = const()[name = string("query_49_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(959148160)))];
            tensor<fp16, [1280]> layers_24_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_24_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(962425024)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_49_cast_fp16 = conv(bias = layers_24_self_attn_q_proj_bias_to_fp16, dilations = query_49_dilations_0, groups = query_49_groups_0, pad = query_49_pad_0, pad_type = query_49_pad_type_0, strides = query_49_strides_0, weight = layers_24_self_attn_q_proj_weight_to_fp16, x = obj_97_cast_fp16)[name = string("query_49_cast_fp16")];
            string key_49_pad_type_0 = const()[name = string("key_49_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_49_strides_0 = const()[name = string("key_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_49_pad_0 = const()[name = string("key_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_49_dilations_0 = const()[name = string("key_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_49_groups_0 = const()[name = string("key_49_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(962427648)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_49_cast_fp16 = conv(dilations = key_49_dilations_0, groups = key_49_groups_0, pad = key_49_pad_0, pad_type = key_49_pad_type_0, strides = key_49_strides_0, weight = layers_24_self_attn_k_proj_weight_to_fp16, x = obj_97_cast_fp16)[name = string("key_49_cast_fp16")];
            string value_49_pad_type_0 = const()[name = string("value_49_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_49_strides_0 = const()[name = string("value_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_49_pad_0 = const()[name = string("value_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_49_dilations_0 = const()[name = string("value_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_49_groups_0 = const()[name = string("value_49_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965704512)))];
            tensor<fp16, [1280]> layers_24_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(968981376)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_49_cast_fp16 = conv(bias = layers_24_self_attn_v_proj_bias_to_fp16, dilations = value_49_dilations_0, groups = value_49_groups_0, pad = value_49_pad_0, pad_type = value_49_pad_type_0, strides = value_49_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = obj_97_cast_fp16)[name = string("value_49_cast_fp16")];
            tensor<int32, [4]> var_3072 = const()[name = string("op_3072"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_49_cast_fp16 = reshape(shape = var_3072, x = query_49_cast_fp16)[name = string("mh_q_49_cast_fp16")];
            fp16 var_3074_to_fp16 = const()[name = string("op_3074_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3075_cast_fp16 = mul(x = mh_q_49_cast_fp16, y = var_3074_to_fp16)[name = string("op_3075_cast_fp16")];
            tensor<int32, [4]> var_3076 = const()[name = string("op_3076"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3077_cast_fp16 = reshape(shape = var_3076, x = key_49_cast_fp16)[name = string("op_3077_cast_fp16")];
            bool mh_w_49_transpose_x_0 = const()[name = string("mh_w_49_transpose_x_0"), val = bool(true)];
            bool mh_w_49_transpose_y_0 = const()[name = string("mh_w_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_49_cast_fp16 = matmul(transpose_x = mh_w_49_transpose_x_0, transpose_y = mh_w_49_transpose_y_0, x = var_3075_cast_fp16, y = var_3077_cast_fp16)[name = string("mh_w_49_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3080_cast_fp16 = softmax(axis = var_3018, x = mh_w_49_cast_fp16)[name = string("op_3080_cast_fp16")];
            tensor<int32, [4]> var_3081 = const()[name = string("op_3081"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3082_cast_fp16 = reshape(shape = var_3081, x = value_49_cast_fp16)[name = string("op_3082_cast_fp16")];
            bool attn_49_transpose_x_0 = const()[name = string("attn_49_transpose_x_0"), val = bool(false)];
            bool attn_49_transpose_y_0 = const()[name = string("attn_49_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3082_cast_fp16, y = var_3080_cast_fp16)[name = string("attn_49_cast_fp16")];
            tensor<int32, [4]> var_3085 = const()[name = string("op_3085"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_193_cast_fp16 = reshape(shape = var_3085, x = attn_49_cast_fp16)[name = string("input_193_cast_fp16")];
            string obj_99_pad_type_0 = const()[name = string("obj_99_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_99_strides_0 = const()[name = string("obj_99_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_99_pad_0 = const()[name = string("obj_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_99_dilations_0 = const()[name = string("obj_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_99_groups_0 = const()[name = string("obj_99_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(968984000)))];
            tensor<fp16, [1280]> layers_24_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_24_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972260864)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_99_cast_fp16 = conv(bias = layers_24_self_attn_o_proj_bias_to_fp16, dilations = obj_99_dilations_0, groups = obj_99_groups_0, pad = obj_99_pad_0, pad_type = obj_99_pad_type_0, strides = obj_99_strides_0, weight = layers_24_self_attn_o_proj_weight_to_fp16, x = input_193_cast_fp16)[name = string("obj_99_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_99_cast_fp16 = add(x = inputs_97_cast_fp16, y = obj_99_cast_fp16)[name = string("inputs_99_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3103_to_fp16 = const()[name = string("op_3103_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_3103_to_fp16, x = inputs_99_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<fp16, [1280]> input_195_gamma_0_to_fp16 = const()[name = string("input_195_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972263488)))];
            tensor<fp16, [1280]> input_195_beta_0_to_fp16 = const()[name = string("input_195_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972266112)))];
            fp16 input_195_epsilon_0_to_fp16 = const()[name = string("input_195_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_195_cast_fp16 = batch_norm(beta = input_195_beta_0_to_fp16, epsilon = input_195_epsilon_0_to_fp16, gamma = input_195_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_99_cast_fp16)[name = string("input_195_cast_fp16")];
            string input_197_pad_type_0 = const()[name = string("input_197_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_197_strides_0 = const()[name = string("input_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_197_pad_0 = const()[name = string("input_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_197_dilations_0 = const()[name = string("input_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_197_groups_0 = const()[name = string("input_197_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_24_fc1_weight_to_fp16 = const()[name = string("layers_24_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972268736)))];
            tensor<fp16, [5120]> layers_24_fc1_bias_to_fp16 = const()[name = string("layers_24_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985376000)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_197_cast_fp16 = conv(bias = layers_24_fc1_bias_to_fp16, dilations = input_197_dilations_0, groups = input_197_groups_0, pad = input_197_pad_0, pad_type = input_197_pad_type_0, strides = input_197_strides_0, weight = layers_24_fc1_weight_to_fp16, x = input_195_cast_fp16)[name = string("input_197_cast_fp16")];
            string input_199_mode_0 = const()[name = string("input_199_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_199_cast_fp16 = gelu(mode = input_199_mode_0, x = input_197_cast_fp16)[name = string("input_199_cast_fp16")];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_24_fc2_weight_to_fp16 = const()[name = string("layers_24_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985386304)))];
            tensor<fp16, [1280]> layers_24_fc2_bias_to_fp16 = const()[name = string("layers_24_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(998493568)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_53_cast_fp16 = conv(bias = layers_24_fc2_bias_to_fp16, dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_24_fc2_weight_to_fp16, x = input_199_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_101_cast_fp16 = add(x = inputs_99_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("inputs_101_cast_fp16")];
            int32 var_3136 = const()[name = string("op_3136"), val = int32(3)];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3155_to_fp16 = const()[name = string("op_3155_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_3155_to_fp16, x = inputs_101_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<fp16, [1280]> obj_101_gamma_0_to_fp16 = const()[name = string("obj_101_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(998496192)))];
            tensor<fp16, [1280]> obj_101_beta_0_to_fp16 = const()[name = string("obj_101_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(998498816)))];
            fp16 obj_101_epsilon_0_to_fp16 = const()[name = string("obj_101_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_101_cast_fp16 = batch_norm(beta = obj_101_beta_0_to_fp16, epsilon = obj_101_epsilon_0_to_fp16, gamma = obj_101_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_101_cast_fp16)[name = string("obj_101_cast_fp16")];
            string query_51_pad_type_0 = const()[name = string("query_51_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_51_strides_0 = const()[name = string("query_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_51_pad_0 = const()[name = string("query_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_51_dilations_0 = const()[name = string("query_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_51_groups_0 = const()[name = string("query_51_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(998501440)))];
            tensor<fp16, [1280]> layers_25_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_25_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1001778304)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_51_cast_fp16 = conv(bias = layers_25_self_attn_q_proj_bias_to_fp16, dilations = query_51_dilations_0, groups = query_51_groups_0, pad = query_51_pad_0, pad_type = query_51_pad_type_0, strides = query_51_strides_0, weight = layers_25_self_attn_q_proj_weight_to_fp16, x = obj_101_cast_fp16)[name = string("query_51_cast_fp16")];
            string key_51_pad_type_0 = const()[name = string("key_51_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_51_strides_0 = const()[name = string("key_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_51_pad_0 = const()[name = string("key_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_51_dilations_0 = const()[name = string("key_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_51_groups_0 = const()[name = string("key_51_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1001780928)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_51_cast_fp16 = conv(dilations = key_51_dilations_0, groups = key_51_groups_0, pad = key_51_pad_0, pad_type = key_51_pad_type_0, strides = key_51_strides_0, weight = layers_25_self_attn_k_proj_weight_to_fp16, x = obj_101_cast_fp16)[name = string("key_51_cast_fp16")];
            string value_51_pad_type_0 = const()[name = string("value_51_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_51_strides_0 = const()[name = string("value_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_51_pad_0 = const()[name = string("value_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_51_dilations_0 = const()[name = string("value_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_51_groups_0 = const()[name = string("value_51_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1005057792)))];
            tensor<fp16, [1280]> layers_25_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1008334656)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_51_cast_fp16 = conv(bias = layers_25_self_attn_v_proj_bias_to_fp16, dilations = value_51_dilations_0, groups = value_51_groups_0, pad = value_51_pad_0, pad_type = value_51_pad_type_0, strides = value_51_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = obj_101_cast_fp16)[name = string("value_51_cast_fp16")];
            tensor<int32, [4]> var_3190 = const()[name = string("op_3190"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_51_cast_fp16 = reshape(shape = var_3190, x = query_51_cast_fp16)[name = string("mh_q_51_cast_fp16")];
            fp16 var_3192_to_fp16 = const()[name = string("op_3192_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3193_cast_fp16 = mul(x = mh_q_51_cast_fp16, y = var_3192_to_fp16)[name = string("op_3193_cast_fp16")];
            tensor<int32, [4]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3195_cast_fp16 = reshape(shape = var_3194, x = key_51_cast_fp16)[name = string("op_3195_cast_fp16")];
            bool mh_w_51_transpose_x_0 = const()[name = string("mh_w_51_transpose_x_0"), val = bool(true)];
            bool mh_w_51_transpose_y_0 = const()[name = string("mh_w_51_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_51_cast_fp16 = matmul(transpose_x = mh_w_51_transpose_x_0, transpose_y = mh_w_51_transpose_y_0, x = var_3193_cast_fp16, y = var_3195_cast_fp16)[name = string("mh_w_51_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3198_cast_fp16 = softmax(axis = var_3136, x = mh_w_51_cast_fp16)[name = string("op_3198_cast_fp16")];
            tensor<int32, [4]> var_3199 = const()[name = string("op_3199"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3200_cast_fp16 = reshape(shape = var_3199, x = value_51_cast_fp16)[name = string("op_3200_cast_fp16")];
            bool attn_51_transpose_x_0 = const()[name = string("attn_51_transpose_x_0"), val = bool(false)];
            bool attn_51_transpose_y_0 = const()[name = string("attn_51_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3200_cast_fp16, y = var_3198_cast_fp16)[name = string("attn_51_cast_fp16")];
            tensor<int32, [4]> var_3203 = const()[name = string("op_3203"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_201_cast_fp16 = reshape(shape = var_3203, x = attn_51_cast_fp16)[name = string("input_201_cast_fp16")];
            string obj_103_pad_type_0 = const()[name = string("obj_103_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_103_strides_0 = const()[name = string("obj_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_103_pad_0 = const()[name = string("obj_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_103_dilations_0 = const()[name = string("obj_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_103_groups_0 = const()[name = string("obj_103_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1008337280)))];
            tensor<fp16, [1280]> layers_25_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_25_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1011614144)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_103_cast_fp16 = conv(bias = layers_25_self_attn_o_proj_bias_to_fp16, dilations = obj_103_dilations_0, groups = obj_103_groups_0, pad = obj_103_pad_0, pad_type = obj_103_pad_type_0, strides = obj_103_strides_0, weight = layers_25_self_attn_o_proj_weight_to_fp16, x = input_201_cast_fp16)[name = string("obj_103_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_103_cast_fp16 = add(x = inputs_101_cast_fp16, y = obj_103_cast_fp16)[name = string("inputs_103_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3221_to_fp16 = const()[name = string("op_3221_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_3221_to_fp16, x = inputs_103_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<fp16, [1280]> input_203_gamma_0_to_fp16 = const()[name = string("input_203_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1011616768)))];
            tensor<fp16, [1280]> input_203_beta_0_to_fp16 = const()[name = string("input_203_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1011619392)))];
            fp16 input_203_epsilon_0_to_fp16 = const()[name = string("input_203_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_203_cast_fp16 = batch_norm(beta = input_203_beta_0_to_fp16, epsilon = input_203_epsilon_0_to_fp16, gamma = input_203_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_103_cast_fp16)[name = string("input_203_cast_fp16")];
            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_25_fc1_weight_to_fp16 = const()[name = string("layers_25_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1011622016)))];
            tensor<fp16, [5120]> layers_25_fc1_bias_to_fp16 = const()[name = string("layers_25_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1024729280)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_205_cast_fp16 = conv(bias = layers_25_fc1_bias_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = layers_25_fc1_weight_to_fp16, x = input_203_cast_fp16)[name = string("input_205_cast_fp16")];
            string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
            string hidden_states_55_pad_type_0 = const()[name = string("hidden_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_55_strides_0 = const()[name = string("hidden_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_55_pad_0 = const()[name = string("hidden_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_55_dilations_0 = const()[name = string("hidden_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_55_groups_0 = const()[name = string("hidden_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_25_fc2_weight_to_fp16 = const()[name = string("layers_25_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1024739584)))];
            tensor<fp16, [1280]> layers_25_fc2_bias_to_fp16 = const()[name = string("layers_25_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1037846848)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_55_cast_fp16 = conv(bias = layers_25_fc2_bias_to_fp16, dilations = hidden_states_55_dilations_0, groups = hidden_states_55_groups_0, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = hidden_states_55_strides_0, weight = layers_25_fc2_weight_to_fp16, x = input_207_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_105_cast_fp16 = add(x = inputs_103_cast_fp16, y = hidden_states_55_cast_fp16)[name = string("inputs_105_cast_fp16")];
            int32 var_3254 = const()[name = string("op_3254"), val = int32(3)];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3273_to_fp16 = const()[name = string("op_3273_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_3273_to_fp16, x = inputs_105_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<fp16, [1280]> obj_105_gamma_0_to_fp16 = const()[name = string("obj_105_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1037849472)))];
            tensor<fp16, [1280]> obj_105_beta_0_to_fp16 = const()[name = string("obj_105_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1037852096)))];
            fp16 obj_105_epsilon_0_to_fp16 = const()[name = string("obj_105_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_105_cast_fp16 = batch_norm(beta = obj_105_beta_0_to_fp16, epsilon = obj_105_epsilon_0_to_fp16, gamma = obj_105_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_105_cast_fp16)[name = string("obj_105_cast_fp16")];
            string query_53_pad_type_0 = const()[name = string("query_53_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_53_strides_0 = const()[name = string("query_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_53_pad_0 = const()[name = string("query_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_53_dilations_0 = const()[name = string("query_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_53_groups_0 = const()[name = string("query_53_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1037854720)))];
            tensor<fp16, [1280]> layers_26_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_26_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1041131584)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_53_cast_fp16 = conv(bias = layers_26_self_attn_q_proj_bias_to_fp16, dilations = query_53_dilations_0, groups = query_53_groups_0, pad = query_53_pad_0, pad_type = query_53_pad_type_0, strides = query_53_strides_0, weight = layers_26_self_attn_q_proj_weight_to_fp16, x = obj_105_cast_fp16)[name = string("query_53_cast_fp16")];
            string key_53_pad_type_0 = const()[name = string("key_53_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_53_strides_0 = const()[name = string("key_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_53_pad_0 = const()[name = string("key_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_53_dilations_0 = const()[name = string("key_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_53_groups_0 = const()[name = string("key_53_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1041134208)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_53_cast_fp16 = conv(dilations = key_53_dilations_0, groups = key_53_groups_0, pad = key_53_pad_0, pad_type = key_53_pad_type_0, strides = key_53_strides_0, weight = layers_26_self_attn_k_proj_weight_to_fp16, x = obj_105_cast_fp16)[name = string("key_53_cast_fp16")];
            string value_53_pad_type_0 = const()[name = string("value_53_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_53_strides_0 = const()[name = string("value_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_53_pad_0 = const()[name = string("value_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_53_dilations_0 = const()[name = string("value_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_53_groups_0 = const()[name = string("value_53_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044411072)))];
            tensor<fp16, [1280]> layers_26_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1047687936)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_53_cast_fp16 = conv(bias = layers_26_self_attn_v_proj_bias_to_fp16, dilations = value_53_dilations_0, groups = value_53_groups_0, pad = value_53_pad_0, pad_type = value_53_pad_type_0, strides = value_53_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = obj_105_cast_fp16)[name = string("value_53_cast_fp16")];
            tensor<int32, [4]> var_3308 = const()[name = string("op_3308"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_53_cast_fp16 = reshape(shape = var_3308, x = query_53_cast_fp16)[name = string("mh_q_53_cast_fp16")];
            fp16 var_3310_to_fp16 = const()[name = string("op_3310_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3311_cast_fp16 = mul(x = mh_q_53_cast_fp16, y = var_3310_to_fp16)[name = string("op_3311_cast_fp16")];
            tensor<int32, [4]> var_3312 = const()[name = string("op_3312"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3313_cast_fp16 = reshape(shape = var_3312, x = key_53_cast_fp16)[name = string("op_3313_cast_fp16")];
            bool mh_w_53_transpose_x_0 = const()[name = string("mh_w_53_transpose_x_0"), val = bool(true)];
            bool mh_w_53_transpose_y_0 = const()[name = string("mh_w_53_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_53_cast_fp16 = matmul(transpose_x = mh_w_53_transpose_x_0, transpose_y = mh_w_53_transpose_y_0, x = var_3311_cast_fp16, y = var_3313_cast_fp16)[name = string("mh_w_53_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3316_cast_fp16 = softmax(axis = var_3254, x = mh_w_53_cast_fp16)[name = string("op_3316_cast_fp16")];
            tensor<int32, [4]> var_3317 = const()[name = string("op_3317"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3318_cast_fp16 = reshape(shape = var_3317, x = value_53_cast_fp16)[name = string("op_3318_cast_fp16")];
            bool attn_53_transpose_x_0 = const()[name = string("attn_53_transpose_x_0"), val = bool(false)];
            bool attn_53_transpose_y_0 = const()[name = string("attn_53_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_3318_cast_fp16, y = var_3316_cast_fp16)[name = string("attn_53_cast_fp16")];
            tensor<int32, [4]> var_3321 = const()[name = string("op_3321"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_209_cast_fp16 = reshape(shape = var_3321, x = attn_53_cast_fp16)[name = string("input_209_cast_fp16")];
            string obj_107_pad_type_0 = const()[name = string("obj_107_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_107_strides_0 = const()[name = string("obj_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_107_pad_0 = const()[name = string("obj_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_107_dilations_0 = const()[name = string("obj_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_107_groups_0 = const()[name = string("obj_107_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1047690560)))];
            tensor<fp16, [1280]> layers_26_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_26_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1050967424)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_107_cast_fp16 = conv(bias = layers_26_self_attn_o_proj_bias_to_fp16, dilations = obj_107_dilations_0, groups = obj_107_groups_0, pad = obj_107_pad_0, pad_type = obj_107_pad_type_0, strides = obj_107_strides_0, weight = layers_26_self_attn_o_proj_weight_to_fp16, x = input_209_cast_fp16)[name = string("obj_107_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_107_cast_fp16 = add(x = inputs_105_cast_fp16, y = obj_107_cast_fp16)[name = string("inputs_107_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3339_to_fp16 = const()[name = string("op_3339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_3339_to_fp16, x = inputs_107_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<fp16, [1280]> input_211_gamma_0_to_fp16 = const()[name = string("input_211_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1050970048)))];
            tensor<fp16, [1280]> input_211_beta_0_to_fp16 = const()[name = string("input_211_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1050972672)))];
            fp16 input_211_epsilon_0_to_fp16 = const()[name = string("input_211_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_211_cast_fp16 = batch_norm(beta = input_211_beta_0_to_fp16, epsilon = input_211_epsilon_0_to_fp16, gamma = input_211_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_107_cast_fp16)[name = string("input_211_cast_fp16")];
            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_26_fc1_weight_to_fp16 = const()[name = string("layers_26_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1050975296)))];
            tensor<fp16, [5120]> layers_26_fc1_bias_to_fp16 = const()[name = string("layers_26_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064082560)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_213_cast_fp16 = conv(bias = layers_26_fc1_bias_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = layers_26_fc1_weight_to_fp16, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")];
            string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_26_fc2_weight_to_fp16 = const()[name = string("layers_26_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064092864)))];
            tensor<fp16, [1280]> layers_26_fc2_bias_to_fp16 = const()[name = string("layers_26_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077200128)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_57_cast_fp16 = conv(bias = layers_26_fc2_bias_to_fp16, dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_26_fc2_weight_to_fp16, x = input_215_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_109_cast_fp16 = add(x = inputs_107_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("inputs_109_cast_fp16")];
            int32 var_3372 = const()[name = string("op_3372"), val = int32(3)];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3391_to_fp16 = const()[name = string("op_3391_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_3391_to_fp16, x = inputs_109_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<fp16, [1280]> obj_109_gamma_0_to_fp16 = const()[name = string("obj_109_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077202752)))];
            tensor<fp16, [1280]> obj_109_beta_0_to_fp16 = const()[name = string("obj_109_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077205376)))];
            fp16 obj_109_epsilon_0_to_fp16 = const()[name = string("obj_109_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_109_cast_fp16 = batch_norm(beta = obj_109_beta_0_to_fp16, epsilon = obj_109_epsilon_0_to_fp16, gamma = obj_109_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_109_cast_fp16)[name = string("obj_109_cast_fp16")];
            string query_55_pad_type_0 = const()[name = string("query_55_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_55_strides_0 = const()[name = string("query_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_55_pad_0 = const()[name = string("query_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_55_dilations_0 = const()[name = string("query_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_55_groups_0 = const()[name = string("query_55_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077208000)))];
            tensor<fp16, [1280]> layers_27_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1080484864)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_55_cast_fp16 = conv(bias = layers_27_self_attn_q_proj_bias_to_fp16, dilations = query_55_dilations_0, groups = query_55_groups_0, pad = query_55_pad_0, pad_type = query_55_pad_type_0, strides = query_55_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = obj_109_cast_fp16)[name = string("query_55_cast_fp16")];
            string key_55_pad_type_0 = const()[name = string("key_55_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_55_strides_0 = const()[name = string("key_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_55_pad_0 = const()[name = string("key_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_55_dilations_0 = const()[name = string("key_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_55_groups_0 = const()[name = string("key_55_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1080487488)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_55_cast_fp16 = conv(dilations = key_55_dilations_0, groups = key_55_groups_0, pad = key_55_pad_0, pad_type = key_55_pad_type_0, strides = key_55_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = obj_109_cast_fp16)[name = string("key_55_cast_fp16")];
            string value_55_pad_type_0 = const()[name = string("value_55_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_55_strides_0 = const()[name = string("value_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_55_pad_0 = const()[name = string("value_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_55_dilations_0 = const()[name = string("value_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_55_groups_0 = const()[name = string("value_55_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1083764352)))];
            tensor<fp16, [1280]> layers_27_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087041216)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_55_cast_fp16 = conv(bias = layers_27_self_attn_v_proj_bias_to_fp16, dilations = value_55_dilations_0, groups = value_55_groups_0, pad = value_55_pad_0, pad_type = value_55_pad_type_0, strides = value_55_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = obj_109_cast_fp16)[name = string("value_55_cast_fp16")];
            tensor<int32, [4]> var_3426 = const()[name = string("op_3426"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_55_cast_fp16 = reshape(shape = var_3426, x = query_55_cast_fp16)[name = string("mh_q_55_cast_fp16")];
            fp16 var_3428_to_fp16 = const()[name = string("op_3428_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3429_cast_fp16 = mul(x = mh_q_55_cast_fp16, y = var_3428_to_fp16)[name = string("op_3429_cast_fp16")];
            tensor<int32, [4]> var_3430 = const()[name = string("op_3430"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3431_cast_fp16 = reshape(shape = var_3430, x = key_55_cast_fp16)[name = string("op_3431_cast_fp16")];
            bool mh_w_55_transpose_x_0 = const()[name = string("mh_w_55_transpose_x_0"), val = bool(true)];
            bool mh_w_55_transpose_y_0 = const()[name = string("mh_w_55_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_55_cast_fp16 = matmul(transpose_x = mh_w_55_transpose_x_0, transpose_y = mh_w_55_transpose_y_0, x = var_3429_cast_fp16, y = var_3431_cast_fp16)[name = string("mh_w_55_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3434_cast_fp16 = softmax(axis = var_3372, x = mh_w_55_cast_fp16)[name = string("op_3434_cast_fp16")];
            tensor<int32, [4]> var_3435 = const()[name = string("op_3435"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3436_cast_fp16 = reshape(shape = var_3435, x = value_55_cast_fp16)[name = string("op_3436_cast_fp16")];
            bool attn_55_transpose_x_0 = const()[name = string("attn_55_transpose_x_0"), val = bool(false)];
            bool attn_55_transpose_y_0 = const()[name = string("attn_55_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_3436_cast_fp16, y = var_3434_cast_fp16)[name = string("attn_55_cast_fp16")];
            tensor<int32, [4]> var_3439 = const()[name = string("op_3439"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_217_cast_fp16 = reshape(shape = var_3439, x = attn_55_cast_fp16)[name = string("input_217_cast_fp16")];
            string obj_111_pad_type_0 = const()[name = string("obj_111_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_111_strides_0 = const()[name = string("obj_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_111_pad_0 = const()[name = string("obj_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_111_dilations_0 = const()[name = string("obj_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_111_groups_0 = const()[name = string("obj_111_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087043840)))];
            tensor<fp16, [1280]> layers_27_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1090320704)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_111_cast_fp16 = conv(bias = layers_27_self_attn_o_proj_bias_to_fp16, dilations = obj_111_dilations_0, groups = obj_111_groups_0, pad = obj_111_pad_0, pad_type = obj_111_pad_type_0, strides = obj_111_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = input_217_cast_fp16)[name = string("obj_111_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_111_cast_fp16 = add(x = inputs_109_cast_fp16, y = obj_111_cast_fp16)[name = string("inputs_111_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3457_to_fp16 = const()[name = string("op_3457_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_3457_to_fp16, x = inputs_111_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<fp16, [1280]> input_219_gamma_0_to_fp16 = const()[name = string("input_219_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1090323328)))];
            tensor<fp16, [1280]> input_219_beta_0_to_fp16 = const()[name = string("input_219_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1090325952)))];
            fp16 input_219_epsilon_0_to_fp16 = const()[name = string("input_219_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_219_cast_fp16 = batch_norm(beta = input_219_beta_0_to_fp16, epsilon = input_219_epsilon_0_to_fp16, gamma = input_219_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_111_cast_fp16)[name = string("input_219_cast_fp16")];
            string input_221_pad_type_0 = const()[name = string("input_221_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_221_strides_0 = const()[name = string("input_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_221_pad_0 = const()[name = string("input_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_221_dilations_0 = const()[name = string("input_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_221_groups_0 = const()[name = string("input_221_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_27_fc1_weight_to_fp16 = const()[name = string("layers_27_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1090328576)))];
            tensor<fp16, [5120]> layers_27_fc1_bias_to_fp16 = const()[name = string("layers_27_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103435840)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_221_cast_fp16 = conv(bias = layers_27_fc1_bias_to_fp16, dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = layers_27_fc1_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
            string input_223_mode_0 = const()[name = string("input_223_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_223_cast_fp16 = gelu(mode = input_223_mode_0, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
            string hidden_states_59_pad_type_0 = const()[name = string("hidden_states_59_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_59_strides_0 = const()[name = string("hidden_states_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_59_pad_0 = const()[name = string("hidden_states_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_59_dilations_0 = const()[name = string("hidden_states_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_59_groups_0 = const()[name = string("hidden_states_59_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_27_fc2_weight_to_fp16 = const()[name = string("layers_27_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103446144)))];
            tensor<fp16, [1280]> layers_27_fc2_bias_to_fp16 = const()[name = string("layers_27_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1116553408)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_59_cast_fp16 = conv(bias = layers_27_fc2_bias_to_fp16, dilations = hidden_states_59_dilations_0, groups = hidden_states_59_groups_0, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = hidden_states_59_strides_0, weight = layers_27_fc2_weight_to_fp16, x = input_223_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_113_cast_fp16 = add(x = inputs_111_cast_fp16, y = hidden_states_59_cast_fp16)[name = string("inputs_113_cast_fp16")];
            int32 var_3490 = const()[name = string("op_3490"), val = int32(3)];
            tensor<int32, [1]> out_113_axes_0 = const()[name = string("out_113_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3509_to_fp16 = const()[name = string("op_3509_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_113_cast_fp16 = layer_norm(axes = out_113_axes_0, epsilon = var_3509_to_fp16, x = inputs_113_cast_fp16)[name = string("out_113_cast_fp16")];
            tensor<fp16, [1280]> obj_113_gamma_0_to_fp16 = const()[name = string("obj_113_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1116556032)))];
            tensor<fp16, [1280]> obj_113_beta_0_to_fp16 = const()[name = string("obj_113_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1116558656)))];
            fp16 obj_113_epsilon_0_to_fp16 = const()[name = string("obj_113_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_113_cast_fp16 = batch_norm(beta = obj_113_beta_0_to_fp16, epsilon = obj_113_epsilon_0_to_fp16, gamma = obj_113_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_113_cast_fp16)[name = string("obj_113_cast_fp16")];
            string query_57_pad_type_0 = const()[name = string("query_57_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_57_strides_0 = const()[name = string("query_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_57_pad_0 = const()[name = string("query_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_57_dilations_0 = const()[name = string("query_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_57_groups_0 = const()[name = string("query_57_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_28_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1116561280)))];
            tensor<fp16, [1280]> layers_28_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_28_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119838144)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_57_cast_fp16 = conv(bias = layers_28_self_attn_q_proj_bias_to_fp16, dilations = query_57_dilations_0, groups = query_57_groups_0, pad = query_57_pad_0, pad_type = query_57_pad_type_0, strides = query_57_strides_0, weight = layers_28_self_attn_q_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = string("query_57_cast_fp16")];
            string key_57_pad_type_0 = const()[name = string("key_57_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_57_strides_0 = const()[name = string("key_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_57_pad_0 = const()[name = string("key_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_57_dilations_0 = const()[name = string("key_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_57_groups_0 = const()[name = string("key_57_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_28_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119840768)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_57_cast_fp16 = conv(dilations = key_57_dilations_0, groups = key_57_groups_0, pad = key_57_pad_0, pad_type = key_57_pad_type_0, strides = key_57_strides_0, weight = layers_28_self_attn_k_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = string("key_57_cast_fp16")];
            string value_57_pad_type_0 = const()[name = string("value_57_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_57_strides_0 = const()[name = string("value_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_57_pad_0 = const()[name = string("value_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_57_dilations_0 = const()[name = string("value_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_57_groups_0 = const()[name = string("value_57_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_28_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1123117632)))];
            tensor<fp16, [1280]> layers_28_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_28_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1126394496)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_57_cast_fp16 = conv(bias = layers_28_self_attn_v_proj_bias_to_fp16, dilations = value_57_dilations_0, groups = value_57_groups_0, pad = value_57_pad_0, pad_type = value_57_pad_type_0, strides = value_57_strides_0, weight = layers_28_self_attn_v_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = string("value_57_cast_fp16")];
            tensor<int32, [4]> var_3544 = const()[name = string("op_3544"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_57_cast_fp16 = reshape(shape = var_3544, x = query_57_cast_fp16)[name = string("mh_q_57_cast_fp16")];
            fp16 var_3546_to_fp16 = const()[name = string("op_3546_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3547_cast_fp16 = mul(x = mh_q_57_cast_fp16, y = var_3546_to_fp16)[name = string("op_3547_cast_fp16")];
            tensor<int32, [4]> var_3548 = const()[name = string("op_3548"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3549_cast_fp16 = reshape(shape = var_3548, x = key_57_cast_fp16)[name = string("op_3549_cast_fp16")];
            bool mh_w_57_transpose_x_0 = const()[name = string("mh_w_57_transpose_x_0"), val = bool(true)];
            bool mh_w_57_transpose_y_0 = const()[name = string("mh_w_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_57_cast_fp16 = matmul(transpose_x = mh_w_57_transpose_x_0, transpose_y = mh_w_57_transpose_y_0, x = var_3547_cast_fp16, y = var_3549_cast_fp16)[name = string("mh_w_57_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3552_cast_fp16 = softmax(axis = var_3490, x = mh_w_57_cast_fp16)[name = string("op_3552_cast_fp16")];
            tensor<int32, [4]> var_3553 = const()[name = string("op_3553"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3554_cast_fp16 = reshape(shape = var_3553, x = value_57_cast_fp16)[name = string("op_3554_cast_fp16")];
            bool attn_57_transpose_x_0 = const()[name = string("attn_57_transpose_x_0"), val = bool(false)];
            bool attn_57_transpose_y_0 = const()[name = string("attn_57_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3554_cast_fp16, y = var_3552_cast_fp16)[name = string("attn_57_cast_fp16")];
            tensor<int32, [4]> var_3557 = const()[name = string("op_3557"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_225_cast_fp16 = reshape(shape = var_3557, x = attn_57_cast_fp16)[name = string("input_225_cast_fp16")];
            string obj_115_pad_type_0 = const()[name = string("obj_115_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_115_strides_0 = const()[name = string("obj_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_115_pad_0 = const()[name = string("obj_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_115_dilations_0 = const()[name = string("obj_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_115_groups_0 = const()[name = string("obj_115_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_28_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1126397120)))];
            tensor<fp16, [1280]> layers_28_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_28_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1129673984)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_115_cast_fp16 = conv(bias = layers_28_self_attn_o_proj_bias_to_fp16, dilations = obj_115_dilations_0, groups = obj_115_groups_0, pad = obj_115_pad_0, pad_type = obj_115_pad_type_0, strides = obj_115_strides_0, weight = layers_28_self_attn_o_proj_weight_to_fp16, x = input_225_cast_fp16)[name = string("obj_115_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_115_cast_fp16 = add(x = inputs_113_cast_fp16, y = obj_115_cast_fp16)[name = string("inputs_115_cast_fp16")];
            tensor<int32, [1]> out_115_axes_0 = const()[name = string("out_115_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3575_to_fp16 = const()[name = string("op_3575_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_115_cast_fp16 = layer_norm(axes = out_115_axes_0, epsilon = var_3575_to_fp16, x = inputs_115_cast_fp16)[name = string("out_115_cast_fp16")];
            tensor<fp16, [1280]> input_227_gamma_0_to_fp16 = const()[name = string("input_227_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1129676608)))];
            tensor<fp16, [1280]> input_227_beta_0_to_fp16 = const()[name = string("input_227_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1129679232)))];
            fp16 input_227_epsilon_0_to_fp16 = const()[name = string("input_227_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_227_cast_fp16 = batch_norm(beta = input_227_beta_0_to_fp16, epsilon = input_227_epsilon_0_to_fp16, gamma = input_227_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_115_cast_fp16)[name = string("input_227_cast_fp16")];
            string input_229_pad_type_0 = const()[name = string("input_229_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_229_strides_0 = const()[name = string("input_229_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_229_pad_0 = const()[name = string("input_229_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_229_dilations_0 = const()[name = string("input_229_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_229_groups_0 = const()[name = string("input_229_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_28_fc1_weight_to_fp16 = const()[name = string("layers_28_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1129681856)))];
            tensor<fp16, [5120]> layers_28_fc1_bias_to_fp16 = const()[name = string("layers_28_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1142789120)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_229_cast_fp16 = conv(bias = layers_28_fc1_bias_to_fp16, dilations = input_229_dilations_0, groups = input_229_groups_0, pad = input_229_pad_0, pad_type = input_229_pad_type_0, strides = input_229_strides_0, weight = layers_28_fc1_weight_to_fp16, x = input_227_cast_fp16)[name = string("input_229_cast_fp16")];
            string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = input_229_cast_fp16)[name = string("input_231_cast_fp16")];
            string hidden_states_61_pad_type_0 = const()[name = string("hidden_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_61_strides_0 = const()[name = string("hidden_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_61_pad_0 = const()[name = string("hidden_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_61_dilations_0 = const()[name = string("hidden_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_61_groups_0 = const()[name = string("hidden_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_28_fc2_weight_to_fp16 = const()[name = string("layers_28_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1142799424)))];
            tensor<fp16, [1280]> layers_28_fc2_bias_to_fp16 = const()[name = string("layers_28_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155906688)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_61_cast_fp16 = conv(bias = layers_28_fc2_bias_to_fp16, dilations = hidden_states_61_dilations_0, groups = hidden_states_61_groups_0, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = hidden_states_61_strides_0, weight = layers_28_fc2_weight_to_fp16, x = input_231_cast_fp16)[name = string("hidden_states_61_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_117_cast_fp16 = add(x = inputs_115_cast_fp16, y = hidden_states_61_cast_fp16)[name = string("inputs_117_cast_fp16")];
            int32 var_3608 = const()[name = string("op_3608"), val = int32(3)];
            tensor<int32, [1]> out_117_axes_0 = const()[name = string("out_117_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3627_to_fp16 = const()[name = string("op_3627_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_117_cast_fp16 = layer_norm(axes = out_117_axes_0, epsilon = var_3627_to_fp16, x = inputs_117_cast_fp16)[name = string("out_117_cast_fp16")];
            tensor<fp16, [1280]> obj_117_gamma_0_to_fp16 = const()[name = string("obj_117_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155909312)))];
            tensor<fp16, [1280]> obj_117_beta_0_to_fp16 = const()[name = string("obj_117_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155911936)))];
            fp16 obj_117_epsilon_0_to_fp16 = const()[name = string("obj_117_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_117_cast_fp16 = batch_norm(beta = obj_117_beta_0_to_fp16, epsilon = obj_117_epsilon_0_to_fp16, gamma = obj_117_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_117_cast_fp16)[name = string("obj_117_cast_fp16")];
            string query_59_pad_type_0 = const()[name = string("query_59_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_59_strides_0 = const()[name = string("query_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_59_pad_0 = const()[name = string("query_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_59_dilations_0 = const()[name = string("query_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_59_groups_0 = const()[name = string("query_59_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_29_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155914560)))];
            tensor<fp16, [1280]> layers_29_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_29_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159191424)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_59_cast_fp16 = conv(bias = layers_29_self_attn_q_proj_bias_to_fp16, dilations = query_59_dilations_0, groups = query_59_groups_0, pad = query_59_pad_0, pad_type = query_59_pad_type_0, strides = query_59_strides_0, weight = layers_29_self_attn_q_proj_weight_to_fp16, x = obj_117_cast_fp16)[name = string("query_59_cast_fp16")];
            string key_59_pad_type_0 = const()[name = string("key_59_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_59_strides_0 = const()[name = string("key_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_59_pad_0 = const()[name = string("key_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_59_dilations_0 = const()[name = string("key_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_59_groups_0 = const()[name = string("key_59_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_29_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159194048)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_59_cast_fp16 = conv(dilations = key_59_dilations_0, groups = key_59_groups_0, pad = key_59_pad_0, pad_type = key_59_pad_type_0, strides = key_59_strides_0, weight = layers_29_self_attn_k_proj_weight_to_fp16, x = obj_117_cast_fp16)[name = string("key_59_cast_fp16")];
            string value_59_pad_type_0 = const()[name = string("value_59_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_59_strides_0 = const()[name = string("value_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_59_pad_0 = const()[name = string("value_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_59_dilations_0 = const()[name = string("value_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_59_groups_0 = const()[name = string("value_59_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_29_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1162470912)))];
            tensor<fp16, [1280]> layers_29_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_29_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1165747776)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_59_cast_fp16 = conv(bias = layers_29_self_attn_v_proj_bias_to_fp16, dilations = value_59_dilations_0, groups = value_59_groups_0, pad = value_59_pad_0, pad_type = value_59_pad_type_0, strides = value_59_strides_0, weight = layers_29_self_attn_v_proj_weight_to_fp16, x = obj_117_cast_fp16)[name = string("value_59_cast_fp16")];
            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_59_cast_fp16 = reshape(shape = var_3662, x = query_59_cast_fp16)[name = string("mh_q_59_cast_fp16")];
            fp16 var_3664_to_fp16 = const()[name = string("op_3664_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3665_cast_fp16 = mul(x = mh_q_59_cast_fp16, y = var_3664_to_fp16)[name = string("op_3665_cast_fp16")];
            tensor<int32, [4]> var_3666 = const()[name = string("op_3666"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3667_cast_fp16 = reshape(shape = var_3666, x = key_59_cast_fp16)[name = string("op_3667_cast_fp16")];
            bool mh_w_59_transpose_x_0 = const()[name = string("mh_w_59_transpose_x_0"), val = bool(true)];
            bool mh_w_59_transpose_y_0 = const()[name = string("mh_w_59_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_59_cast_fp16 = matmul(transpose_x = mh_w_59_transpose_x_0, transpose_y = mh_w_59_transpose_y_0, x = var_3665_cast_fp16, y = var_3667_cast_fp16)[name = string("mh_w_59_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3670_cast_fp16 = softmax(axis = var_3608, x = mh_w_59_cast_fp16)[name = string("op_3670_cast_fp16")];
            tensor<int32, [4]> var_3671 = const()[name = string("op_3671"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3672_cast_fp16 = reshape(shape = var_3671, x = value_59_cast_fp16)[name = string("op_3672_cast_fp16")];
            bool attn_59_transpose_x_0 = const()[name = string("attn_59_transpose_x_0"), val = bool(false)];
            bool attn_59_transpose_y_0 = const()[name = string("attn_59_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3672_cast_fp16, y = var_3670_cast_fp16)[name = string("attn_59_cast_fp16")];
            tensor<int32, [4]> var_3675 = const()[name = string("op_3675"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_233_cast_fp16 = reshape(shape = var_3675, x = attn_59_cast_fp16)[name = string("input_233_cast_fp16")];
            string obj_119_pad_type_0 = const()[name = string("obj_119_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_119_strides_0 = const()[name = string("obj_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_119_pad_0 = const()[name = string("obj_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_119_dilations_0 = const()[name = string("obj_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_119_groups_0 = const()[name = string("obj_119_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_29_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1165750400)))];
            tensor<fp16, [1280]> layers_29_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_29_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1169027264)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_119_cast_fp16 = conv(bias = layers_29_self_attn_o_proj_bias_to_fp16, dilations = obj_119_dilations_0, groups = obj_119_groups_0, pad = obj_119_pad_0, pad_type = obj_119_pad_type_0, strides = obj_119_strides_0, weight = layers_29_self_attn_o_proj_weight_to_fp16, x = input_233_cast_fp16)[name = string("obj_119_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_119_cast_fp16 = add(x = inputs_117_cast_fp16, y = obj_119_cast_fp16)[name = string("inputs_119_cast_fp16")];
            tensor<int32, [1]> out_119_axes_0 = const()[name = string("out_119_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3693_to_fp16 = const()[name = string("op_3693_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_119_cast_fp16 = layer_norm(axes = out_119_axes_0, epsilon = var_3693_to_fp16, x = inputs_119_cast_fp16)[name = string("out_119_cast_fp16")];
            tensor<fp16, [1280]> input_235_gamma_0_to_fp16 = const()[name = string("input_235_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1169029888)))];
            tensor<fp16, [1280]> input_235_beta_0_to_fp16 = const()[name = string("input_235_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1169032512)))];
            fp16 input_235_epsilon_0_to_fp16 = const()[name = string("input_235_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_235_cast_fp16 = batch_norm(beta = input_235_beta_0_to_fp16, epsilon = input_235_epsilon_0_to_fp16, gamma = input_235_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_119_cast_fp16)[name = string("input_235_cast_fp16")];
            string input_237_pad_type_0 = const()[name = string("input_237_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_237_strides_0 = const()[name = string("input_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_237_dilations_0 = const()[name = string("input_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_237_groups_0 = const()[name = string("input_237_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_29_fc1_weight_to_fp16 = const()[name = string("layers_29_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1169035136)))];
            tensor<fp16, [5120]> layers_29_fc1_bias_to_fp16 = const()[name = string("layers_29_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1182142400)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_237_cast_fp16 = conv(bias = layers_29_fc1_bias_to_fp16, dilations = input_237_dilations_0, groups = input_237_groups_0, pad = input_237_pad_0, pad_type = input_237_pad_type_0, strides = input_237_strides_0, weight = layers_29_fc1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
            string input_239_mode_0 = const()[name = string("input_239_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_239_cast_fp16 = gelu(mode = input_239_mode_0, x = input_237_cast_fp16)[name = string("input_239_cast_fp16")];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_29_fc2_weight_to_fp16 = const()[name = string("layers_29_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1182152704)))];
            tensor<fp16, [1280]> layers_29_fc2_bias_to_fp16 = const()[name = string("layers_29_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195259968)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_63_cast_fp16 = conv(bias = layers_29_fc2_bias_to_fp16, dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_29_fc2_weight_to_fp16, x = input_239_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_121_cast_fp16 = add(x = inputs_119_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("inputs_121_cast_fp16")];
            int32 var_3726 = const()[name = string("op_3726"), val = int32(3)];
            tensor<int32, [1]> out_121_axes_0 = const()[name = string("out_121_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3745_to_fp16 = const()[name = string("op_3745_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_121_cast_fp16 = layer_norm(axes = out_121_axes_0, epsilon = var_3745_to_fp16, x = inputs_121_cast_fp16)[name = string("out_121_cast_fp16")];
            tensor<fp16, [1280]> obj_121_gamma_0_to_fp16 = const()[name = string("obj_121_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195262592)))];
            tensor<fp16, [1280]> obj_121_beta_0_to_fp16 = const()[name = string("obj_121_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195265216)))];
            fp16 obj_121_epsilon_0_to_fp16 = const()[name = string("obj_121_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_121_cast_fp16 = batch_norm(beta = obj_121_beta_0_to_fp16, epsilon = obj_121_epsilon_0_to_fp16, gamma = obj_121_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_121_cast_fp16)[name = string("obj_121_cast_fp16")];
            string query_61_pad_type_0 = const()[name = string("query_61_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_61_strides_0 = const()[name = string("query_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_61_pad_0 = const()[name = string("query_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_61_dilations_0 = const()[name = string("query_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_61_groups_0 = const()[name = string("query_61_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_30_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195267840)))];
            tensor<fp16, [1280]> layers_30_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_30_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1198544704)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_61_cast_fp16 = conv(bias = layers_30_self_attn_q_proj_bias_to_fp16, dilations = query_61_dilations_0, groups = query_61_groups_0, pad = query_61_pad_0, pad_type = query_61_pad_type_0, strides = query_61_strides_0, weight = layers_30_self_attn_q_proj_weight_to_fp16, x = obj_121_cast_fp16)[name = string("query_61_cast_fp16")];
            string key_61_pad_type_0 = const()[name = string("key_61_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_61_strides_0 = const()[name = string("key_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_61_pad_0 = const()[name = string("key_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_61_dilations_0 = const()[name = string("key_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_61_groups_0 = const()[name = string("key_61_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_30_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1198547328)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_61_cast_fp16 = conv(dilations = key_61_dilations_0, groups = key_61_groups_0, pad = key_61_pad_0, pad_type = key_61_pad_type_0, strides = key_61_strides_0, weight = layers_30_self_attn_k_proj_weight_to_fp16, x = obj_121_cast_fp16)[name = string("key_61_cast_fp16")];
            string value_61_pad_type_0 = const()[name = string("value_61_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_61_strides_0 = const()[name = string("value_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_61_pad_0 = const()[name = string("value_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_61_dilations_0 = const()[name = string("value_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_61_groups_0 = const()[name = string("value_61_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_30_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201824192)))];
            tensor<fp16, [1280]> layers_30_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_30_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205101056)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_61_cast_fp16 = conv(bias = layers_30_self_attn_v_proj_bias_to_fp16, dilations = value_61_dilations_0, groups = value_61_groups_0, pad = value_61_pad_0, pad_type = value_61_pad_type_0, strides = value_61_strides_0, weight = layers_30_self_attn_v_proj_weight_to_fp16, x = obj_121_cast_fp16)[name = string("value_61_cast_fp16")];
            tensor<int32, [4]> var_3780 = const()[name = string("op_3780"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_61_cast_fp16 = reshape(shape = var_3780, x = query_61_cast_fp16)[name = string("mh_q_61_cast_fp16")];
            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3783_cast_fp16 = mul(x = mh_q_61_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
            tensor<int32, [4]> var_3784 = const()[name = string("op_3784"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3785_cast_fp16 = reshape(shape = var_3784, x = key_61_cast_fp16)[name = string("op_3785_cast_fp16")];
            bool mh_w_61_transpose_x_0 = const()[name = string("mh_w_61_transpose_x_0"), val = bool(true)];
            bool mh_w_61_transpose_y_0 = const()[name = string("mh_w_61_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_61_cast_fp16 = matmul(transpose_x = mh_w_61_transpose_x_0, transpose_y = mh_w_61_transpose_y_0, x = var_3783_cast_fp16, y = var_3785_cast_fp16)[name = string("mh_w_61_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3788_cast_fp16 = softmax(axis = var_3726, x = mh_w_61_cast_fp16)[name = string("op_3788_cast_fp16")];
            tensor<int32, [4]> var_3789 = const()[name = string("op_3789"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3790_cast_fp16 = reshape(shape = var_3789, x = value_61_cast_fp16)[name = string("op_3790_cast_fp16")];
            bool attn_61_transpose_x_0 = const()[name = string("attn_61_transpose_x_0"), val = bool(false)];
            bool attn_61_transpose_y_0 = const()[name = string("attn_61_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3790_cast_fp16, y = var_3788_cast_fp16)[name = string("attn_61_cast_fp16")];
            tensor<int32, [4]> var_3793 = const()[name = string("op_3793"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_241_cast_fp16 = reshape(shape = var_3793, x = attn_61_cast_fp16)[name = string("input_241_cast_fp16")];
            string obj_123_pad_type_0 = const()[name = string("obj_123_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_123_strides_0 = const()[name = string("obj_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_123_pad_0 = const()[name = string("obj_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_123_dilations_0 = const()[name = string("obj_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_123_groups_0 = const()[name = string("obj_123_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_30_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205103680)))];
            tensor<fp16, [1280]> layers_30_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_30_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1208380544)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_123_cast_fp16 = conv(bias = layers_30_self_attn_o_proj_bias_to_fp16, dilations = obj_123_dilations_0, groups = obj_123_groups_0, pad = obj_123_pad_0, pad_type = obj_123_pad_type_0, strides = obj_123_strides_0, weight = layers_30_self_attn_o_proj_weight_to_fp16, x = input_241_cast_fp16)[name = string("obj_123_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_123_cast_fp16 = add(x = inputs_121_cast_fp16, y = obj_123_cast_fp16)[name = string("inputs_123_cast_fp16")];
            tensor<int32, [1]> out_123_axes_0 = const()[name = string("out_123_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3811_to_fp16 = const()[name = string("op_3811_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_123_cast_fp16 = layer_norm(axes = out_123_axes_0, epsilon = var_3811_to_fp16, x = inputs_123_cast_fp16)[name = string("out_123_cast_fp16")];
            tensor<fp16, [1280]> input_243_gamma_0_to_fp16 = const()[name = string("input_243_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1208383168)))];
            tensor<fp16, [1280]> input_243_beta_0_to_fp16 = const()[name = string("input_243_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1208385792)))];
            fp16 input_243_epsilon_0_to_fp16 = const()[name = string("input_243_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_243_cast_fp16 = batch_norm(beta = input_243_beta_0_to_fp16, epsilon = input_243_epsilon_0_to_fp16, gamma = input_243_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_123_cast_fp16)[name = string("input_243_cast_fp16")];
            string input_245_pad_type_0 = const()[name = string("input_245_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_245_strides_0 = const()[name = string("input_245_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_245_pad_0 = const()[name = string("input_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_245_dilations_0 = const()[name = string("input_245_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_245_groups_0 = const()[name = string("input_245_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_30_fc1_weight_to_fp16 = const()[name = string("layers_30_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1208388416)))];
            tensor<fp16, [5120]> layers_30_fc1_bias_to_fp16 = const()[name = string("layers_30_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1221495680)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_245_cast_fp16 = conv(bias = layers_30_fc1_bias_to_fp16, dilations = input_245_dilations_0, groups = input_245_groups_0, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = input_245_strides_0, weight = layers_30_fc1_weight_to_fp16, x = input_243_cast_fp16)[name = string("input_245_cast_fp16")];
            string input_247_mode_0 = const()[name = string("input_247_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_247_cast_fp16 = gelu(mode = input_247_mode_0, x = input_245_cast_fp16)[name = string("input_247_cast_fp16")];
            string hidden_states_65_pad_type_0 = const()[name = string("hidden_states_65_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_65_strides_0 = const()[name = string("hidden_states_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_65_pad_0 = const()[name = string("hidden_states_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_65_dilations_0 = const()[name = string("hidden_states_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_65_groups_0 = const()[name = string("hidden_states_65_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_30_fc2_weight_to_fp16 = const()[name = string("layers_30_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1221505984)))];
            tensor<fp16, [1280]> layers_30_fc2_bias_to_fp16 = const()[name = string("layers_30_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234613248)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_65_cast_fp16 = conv(bias = layers_30_fc2_bias_to_fp16, dilations = hidden_states_65_dilations_0, groups = hidden_states_65_groups_0, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = hidden_states_65_strides_0, weight = layers_30_fc2_weight_to_fp16, x = input_247_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_125_cast_fp16 = add(x = inputs_123_cast_fp16, y = hidden_states_65_cast_fp16)[name = string("inputs_125_cast_fp16")];
            int32 var_3844 = const()[name = string("op_3844"), val = int32(3)];
            tensor<int32, [1]> out_125_axes_0 = const()[name = string("out_125_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3863_to_fp16 = const()[name = string("op_3863_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_125_cast_fp16 = layer_norm(axes = out_125_axes_0, epsilon = var_3863_to_fp16, x = inputs_125_cast_fp16)[name = string("out_125_cast_fp16")];
            tensor<fp16, [1280]> obj_125_gamma_0_to_fp16 = const()[name = string("obj_125_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234615872)))];
            tensor<fp16, [1280]> obj_125_beta_0_to_fp16 = const()[name = string("obj_125_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234618496)))];
            fp16 obj_125_epsilon_0_to_fp16 = const()[name = string("obj_125_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> obj_125_cast_fp16 = batch_norm(beta = obj_125_beta_0_to_fp16, epsilon = obj_125_epsilon_0_to_fp16, gamma = obj_125_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_125_cast_fp16)[name = string("obj_125_cast_fp16")];
            string query_pad_type_0 = const()[name = string("query_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> query_strides_0 = const()[name = string("query_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> query_pad_0 = const()[name = string("query_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_dilations_0 = const()[name = string("query_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_groups_0 = const()[name = string("query_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_31_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234621120)))];
            tensor<fp16, [1280]> layers_31_self_attn_q_proj_bias_to_fp16 = const()[name = string("layers_31_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1237897984)))];
            tensor<fp16, [1, 1280, 1, 1500]> query_cast_fp16 = conv(bias = layers_31_self_attn_q_proj_bias_to_fp16, dilations = query_dilations_0, groups = query_groups_0, pad = query_pad_0, pad_type = query_pad_type_0, strides = query_strides_0, weight = layers_31_self_attn_q_proj_weight_to_fp16, x = obj_125_cast_fp16)[name = string("query_cast_fp16")];
            string key_pad_type_0 = const()[name = string("key_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> key_strides_0 = const()[name = string("key_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> key_pad_0 = const()[name = string("key_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_dilations_0 = const()[name = string("key_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_groups_0 = const()[name = string("key_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_31_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1237900608)))];
            tensor<fp16, [1, 1280, 1, 1500]> key_cast_fp16 = conv(dilations = key_dilations_0, groups = key_groups_0, pad = key_pad_0, pad_type = key_pad_type_0, strides = key_strides_0, weight = layers_31_self_attn_k_proj_weight_to_fp16, x = obj_125_cast_fp16)[name = string("key_cast_fp16")];
            string value_pad_type_0 = const()[name = string("value_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> value_strides_0 = const()[name = string("value_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> value_pad_0 = const()[name = string("value_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_dilations_0 = const()[name = string("value_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_groups_0 = const()[name = string("value_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_31_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1241177472)))];
            tensor<fp16, [1280]> layers_31_self_attn_v_proj_bias_to_fp16 = const()[name = string("layers_31_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1244454336)))];
            tensor<fp16, [1, 1280, 1, 1500]> value_cast_fp16 = conv(bias = layers_31_self_attn_v_proj_bias_to_fp16, dilations = value_dilations_0, groups = value_groups_0, pad = value_pad_0, pad_type = value_pad_type_0, strides = value_strides_0, weight = layers_31_self_attn_v_proj_weight_to_fp16, x = obj_125_cast_fp16)[name = string("value_cast_fp16")];
            tensor<int32, [4]> var_3898 = const()[name = string("op_3898"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> mh_q_cast_fp16 = reshape(shape = var_3898, x = query_cast_fp16)[name = string("mh_q_cast_fp16")];
            fp16 var_3900_to_fp16 = const()[name = string("op_3900_to_fp16"), val = fp16(0x1p-3)];
            tensor<fp16, [1, 20, 64, 1500]> var_3901_cast_fp16 = mul(x = mh_q_cast_fp16, y = var_3900_to_fp16)[name = string("op_3901_cast_fp16")];
            tensor<int32, [4]> var_3902 = const()[name = string("op_3902"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3903_cast_fp16 = reshape(shape = var_3902, x = key_cast_fp16)[name = string("op_3903_cast_fp16")];
            bool mh_w_transpose_x_0 = const()[name = string("mh_w_transpose_x_0"), val = bool(true)];
            bool mh_w_transpose_y_0 = const()[name = string("mh_w_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 20, 1500, 1500]> mh_w_cast_fp16 = matmul(transpose_x = mh_w_transpose_x_0, transpose_y = mh_w_transpose_y_0, x = var_3901_cast_fp16, y = var_3903_cast_fp16)[name = string("mh_w_cast_fp16")];
            tensor<fp16, [1, 20, 1500, 1500]> var_3906_cast_fp16 = softmax(axis = var_3844, x = mh_w_cast_fp16)[name = string("op_3906_cast_fp16")];
            tensor<int32, [4]> var_3907 = const()[name = string("op_3907"), val = tensor<int32, [4]>([1, 20, 64, -1])];
            tensor<fp16, [1, 20, 64, 1500]> var_3908_cast_fp16 = reshape(shape = var_3907, x = value_cast_fp16)[name = string("op_3908_cast_fp16")];
            bool attn_transpose_x_0 = const()[name = string("attn_transpose_x_0"), val = bool(false)];
            bool attn_transpose_y_0 = const()[name = string("attn_transpose_y_0"), val = bool(true)];
            tensor<fp16, [1, 20, 64, 1500]> attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_3908_cast_fp16, y = var_3906_cast_fp16)[name = string("attn_cast_fp16")];
            tensor<int32, [4]> var_3911 = const()[name = string("op_3911"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
            tensor<fp16, [1, 1280, 1, 1500]> input_249_cast_fp16 = reshape(shape = var_3911, x = attn_cast_fp16)[name = string("input_249_cast_fp16")];
            string obj_pad_type_0 = const()[name = string("obj_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> obj_strides_0 = const()[name = string("obj_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> obj_pad_0 = const()[name = string("obj_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> obj_dilations_0 = const()[name = string("obj_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 obj_groups_0 = const()[name = string("obj_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_31_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1244456960)))];
            tensor<fp16, [1280]> layers_31_self_attn_o_proj_bias_to_fp16 = const()[name = string("layers_31_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1247733824)))];
            tensor<fp16, [1, 1280, 1, 1500]> obj_cast_fp16 = conv(bias = layers_31_self_attn_o_proj_bias_to_fp16, dilations = obj_dilations_0, groups = obj_groups_0, pad = obj_pad_0, pad_type = obj_pad_type_0, strides = obj_strides_0, weight = layers_31_self_attn_o_proj_weight_to_fp16, x = input_249_cast_fp16)[name = string("obj_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_127_cast_fp16 = add(x = inputs_125_cast_fp16, y = obj_cast_fp16)[name = string("inputs_127_cast_fp16")];
            tensor<int32, [1]> out_127_axes_0 = const()[name = string("out_127_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3929_to_fp16 = const()[name = string("op_3929_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_127_cast_fp16 = layer_norm(axes = out_127_axes_0, epsilon = var_3929_to_fp16, x = inputs_127_cast_fp16)[name = string("out_127_cast_fp16")];
            tensor<fp16, [1280]> input_251_gamma_0_to_fp16 = const()[name = string("input_251_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1247736448)))];
            tensor<fp16, [1280]> input_251_beta_0_to_fp16 = const()[name = string("input_251_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1247739072)))];
            fp16 input_251_epsilon_0_to_fp16 = const()[name = string("input_251_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> input_251_cast_fp16 = batch_norm(beta = input_251_beta_0_to_fp16, epsilon = input_251_epsilon_0_to_fp16, gamma = input_251_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_127_cast_fp16)[name = string("input_251_cast_fp16")];
            string input_253_pad_type_0 = const()[name = string("input_253_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_253_strides_0 = const()[name = string("input_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_253_pad_0 = const()[name = string("input_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_253_dilations_0 = const()[name = string("input_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_253_groups_0 = const()[name = string("input_253_groups_0"), val = int32(1)];
            tensor<fp16, [5120, 1280, 1, 1]> layers_31_fc1_weight_to_fp16 = const()[name = string("layers_31_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1247741696)))];
            tensor<fp16, [5120]> layers_31_fc1_bias_to_fp16 = const()[name = string("layers_31_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1260848960)))];
            tensor<fp16, [1, 5120, 1, 1500]> input_253_cast_fp16 = conv(bias = layers_31_fc1_bias_to_fp16, dilations = input_253_dilations_0, groups = input_253_groups_0, pad = input_253_pad_0, pad_type = input_253_pad_type_0, strides = input_253_strides_0, weight = layers_31_fc1_weight_to_fp16, x = input_251_cast_fp16)[name = string("input_253_cast_fp16")];
            string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 5120, 1, 1500]> input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")];
            string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 5120, 1, 1]> layers_31_fc2_weight_to_fp16 = const()[name = string("layers_31_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1260859264)))];
            tensor<fp16, [1280]> layers_31_fc2_bias_to_fp16 = const()[name = string("layers_31_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273966528)))];
            tensor<fp16, [1, 1280, 1, 1500]> hidden_states_cast_fp16 = conv(bias = layers_31_fc2_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = layers_31_fc2_weight_to_fp16, x = input_255_cast_fp16)[name = string("hidden_states_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1500]> inputs_cast_fp16 = add(x = inputs_127_cast_fp16, y = hidden_states_cast_fp16)[name = string("inputs_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3967_to_fp16 = const()[name = string("op_3967_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_3967_to_fp16, x = inputs_cast_fp16)[name = string("out_cast_fp16")];
            tensor<fp16, [1280]> encoder_output_embeds_type_fp32_gamma_0_to_fp16 = const()[name = string("encoder_output_embeds_type_fp32_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273969152)))];
            tensor<fp16, [1280]> encoder_output_embeds_type_fp32_beta_0_to_fp16 = const()[name = string("encoder_output_embeds_type_fp32_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273971776)))];
            fp16 encoder_output_embeds_type_fp32_epsilon_0_to_fp16 = const()[name = string("encoder_output_embeds_type_fp32_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1280, 1, 1500]> encoder_output_embeds = batch_norm(beta = encoder_output_embeds_type_fp32_beta_0_to_fp16, epsilon = encoder_output_embeds_type_fp32_epsilon_0_to_fp16, gamma = encoder_output_embeds_type_fp32_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_cast_fp16)[name = string("encoder_output_embeds_type_fp32_cast_fp16")];
            string var_3991_pad_type_0 = const()[name = string("op_3991_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_3991_strides_0 = const()[name = string("op_3991_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_3991_pad_0 = const()[name = string("op_3991_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3991_dilations_0 = const()[name = string("op_3991_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3991_groups_0 = const()[name = string("op_3991_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_0_encoder_attn_k_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_0_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273974400)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_3991_cast_fp16 = conv(dilations = var_3991_dilations_0, groups = var_3991_groups_0, pad = var_3991_pad_0, pad_type = var_3991_pad_type_0, strides = var_3991_strides_0, weight = decoder_kv_cache_prep_0_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_3991_cast_fp16")];
            string var_3998_pad_type_0 = const()[name = string("op_3998_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_3998_strides_0 = const()[name = string("op_3998_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_3998_pad_0 = const()[name = string("op_3998_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3998_dilations_0 = const()[name = string("op_3998_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3998_groups_0 = const()[name = string("op_3998_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_0_encoder_attn_v_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_0_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1277251264)))];
            tensor<fp16, [1280]> decoder_kv_cache_prep_0_encoder_attn_v_proj_bias_to_fp16 = const()[name = string("decoder_kv_cache_prep_0_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280528128)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_3998_cast_fp16 = conv(bias = decoder_kv_cache_prep_0_encoder_attn_v_proj_bias_to_fp16, dilations = var_3998_dilations_0, groups = var_3998_groups_0, pad = var_3998_pad_0, pad_type = var_3998_pad_type_0, strides = var_3998_strides_0, weight = decoder_kv_cache_prep_0_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_3998_cast_fp16")];
            string var_4016_pad_type_0 = const()[name = string("op_4016_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_4016_strides_0 = const()[name = string("op_4016_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_4016_pad_0 = const()[name = string("op_4016_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4016_dilations_0 = const()[name = string("op_4016_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4016_groups_0 = const()[name = string("op_4016_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_1_encoder_attn_k_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_1_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280530752)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_4016_cast_fp16 = conv(dilations = var_4016_dilations_0, groups = var_4016_groups_0, pad = var_4016_pad_0, pad_type = var_4016_pad_type_0, strides = var_4016_strides_0, weight = decoder_kv_cache_prep_1_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_4016_cast_fp16")];
            string var_4023_pad_type_0 = const()[name = string("op_4023_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_4023_strides_0 = const()[name = string("op_4023_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_4023_pad_0 = const()[name = string("op_4023_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4023_dilations_0 = const()[name = string("op_4023_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4023_groups_0 = const()[name = string("op_4023_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_1_encoder_attn_v_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_1_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283807616)))];
            tensor<fp16, [1280]> decoder_kv_cache_prep_1_encoder_attn_v_proj_bias_to_fp16 = const()[name = string("decoder_kv_cache_prep_1_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1287084480)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_4023_cast_fp16 = conv(bias = decoder_kv_cache_prep_1_encoder_attn_v_proj_bias_to_fp16, dilations = var_4023_dilations_0, groups = var_4023_groups_0, pad = var_4023_pad_0, pad_type = var_4023_pad_type_0, strides = var_4023_strides_0, weight = decoder_kv_cache_prep_1_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_4023_cast_fp16")];
            string var_4041_pad_type_0 = const()[name = string("op_4041_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_4041_strides_0 = const()[name = string("op_4041_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_4041_pad_0 = const()[name = string("op_4041_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4041_dilations_0 = const()[name = string("op_4041_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4041_groups_0 = const()[name = string("op_4041_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_2_encoder_attn_k_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_2_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1287087104)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_4041_cast_fp16 = conv(dilations = var_4041_dilations_0, groups = var_4041_groups_0, pad = var_4041_pad_0, pad_type = var_4041_pad_type_0, strides = var_4041_strides_0, weight = decoder_kv_cache_prep_2_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_4041_cast_fp16")];
            string var_4048_pad_type_0 = const()[name = string("op_4048_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> var_4048_strides_0 = const()[name = string("op_4048_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> var_4048_pad_0 = const()[name = string("op_4048_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4048_dilations_0 = const()[name = string("op_4048_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4048_groups_0 = const()[name = string("op_4048_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_2_encoder_attn_v_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_2_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1290363968)))];
            tensor<fp16, [1280]> decoder_kv_cache_prep_2_encoder_attn_v_proj_bias_to_fp16 = const()[name = string("decoder_kv_cache_prep_2_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1293640832)))];
            tensor<fp16, [1, 1280, 1, 1500]> var_4048_cast_fp16 = conv(bias = decoder_kv_cache_prep_2_encoder_attn_v_proj_bias_to_fp16, dilations = var_4048_dilations_0, groups = var_4048_groups_0, pad = var_4048_pad_0, pad_type = var_4048_pad_type_0, strides = var_4048_strides_0, weight = decoder_kv_cache_prep_2_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("op_4048_cast_fp16")];
            string k_pad_type_0 = const()[name = string("k_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> k_strides_0 = const()[name = string("k_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> k_pad_0 = const()[name = string("k_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> k_dilations_0 = const()[name = string("k_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 k_groups_0 = const()[name = string("k_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_3_encoder_attn_k_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_3_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1293643456)))];
            tensor<fp16, [1, 1280, 1, 1500]> k_cast_fp16 = conv(dilations = k_dilations_0, groups = k_groups_0, pad = k_pad_0, pad_type = k_pad_type_0, strides = k_strides_0, weight = decoder_kv_cache_prep_3_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("k_cast_fp16")];
            string v_pad_type_0 = const()[name = string("v_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> v_strides_0 = const()[name = string("v_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> v_pad_0 = const()[name = string("v_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> v_dilations_0 = const()[name = string("v_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 v_groups_0 = const()[name = string("v_groups_0"), val = int32(1)];
            tensor<fp16, [1280, 1280, 1, 1]> decoder_kv_cache_prep_3_encoder_attn_v_proj_weight_to_fp16 = const()[name = string("decoder_kv_cache_prep_3_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1296920320)))];
            tensor<fp16, [1280]> decoder_kv_cache_prep_3_encoder_attn_v_proj_bias_to_fp16 = const()[name = string("decoder_kv_cache_prep_3_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1300197184)))];
            tensor<fp16, [1, 1280, 1, 1500]> v_cast_fp16 = conv(bias = decoder_kv_cache_prep_3_encoder_attn_v_proj_bias_to_fp16, dilations = v_dilations_0, groups = v_groups_0, pad = v_pad_0, pad_type = v_pad_type_0, strides = v_strides_0, weight = decoder_kv_cache_prep_3_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = string("v_cast_fp16")];
            int32 var_4078 = const()[name = string("op_4078"), val = int32(0)];
            bool input_259_interleave_0 = const()[name = string("input_259_interleave_0"), val = bool(false)];
            tensor<fp16, [4, 1280, 1, 1500]> input_259_cast_fp16 = concat(axis = var_4078, interleave = input_259_interleave_0, values = (var_3991_cast_fp16, var_4016_cast_fp16, var_4041_cast_fp16, k_cast_fp16))[name = string("input_259_cast_fp16")];
            int32 var_4081 = const()[name = string("op_4081"), val = int32(0)];
            bool input_interleave_0 = const()[name = string("input_interleave_0"), val = bool(false)];
            tensor<fp16, [4, 1280, 1, 1500]> input_cast_fp16 = concat(axis = var_4081, interleave = input_interleave_0, values = (var_3998_cast_fp16, var_4023_cast_fp16, var_4048_cast_fp16, v_cast_fp16))[name = string("input_cast_fp16")];
            tensor<int32, [8]> var_4088_pad_0 = const()[name = string("op_4088_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 36])];
            string var_4088_mode_0 = const()[name = string("op_4088_mode_0"), val = string("constant")];
            fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [4, 1280, 1, 1536]> encoder_attn_key_cache = pad(constant_val = const_33_to_fp16, mode = var_4088_mode_0, pad = var_4088_pad_0, x = input_259_cast_fp16)[name = string("op_4088_cast_fp16")];
            tensor<int32, [8]> var_4094_pad_0 = const()[name = string("op_4094_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 36])];
            string var_4094_mode_0 = const()[name = string("op_4094_mode_0"), val = string("constant")];
            fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [4, 1280, 1, 1536]> encoder_attn_value_cache = pad(constant_val = const_34_to_fp16, mode = var_4094_mode_0, pad = var_4094_pad_0, x = input_cast_fp16)[name = string("op_4094_cast_fp16")];
        } -> (encoder_output_embeds, encoder_attn_key_cache, encoder_attn_value_cache);
}