# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/xc/cxceqiy6okvqxrjbwoo7ygc4plr32q3u54opf7nc2ufvkbmb4dzh.py
# Topologically Sorted Source Nodes: [stacked_inputs], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   stacked_inputs => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/c2/cc2dm6t5shxvlslm54ib7dbfegegnre2lr6ktlpilmy7q5rgj3he.py
# Topologically Sorted Source Nodes: [xi_1, xf_1, xo_1, xg_1, mul, mul_1, c, tanh_1, h], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   c => add
#   h => mul_2
#   mul => mul
#   mul_1 => mul_1
#   tanh_1 => tanh_1
#   xf_1 => sigmoid_1
#   xg_1 => tanh
#   xi_1 => sigmoid
#   xo_1 => sigmoid_2
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_2,), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_3), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 36)
    x4 = xindex % 36
    x1 = (xindex // 9) % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (72 + x4 + (144*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x4 + (144*x2)), xmask)
    tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (108 + x4 + (144*x2)), xmask)
    tmp9 = tl.load(in_ptr1 + (12 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (36 + x4 + (144*x2)), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp7 * tmp11
    tmp19 = tmp17 + tmp18
    tmp20 = 1.0
    tmp21 = tmp20 - tmp15
    tmp22 = tmp15 * tmp21
    tmp23 = libdevice.tanh(tmp19)
    tmp24 = tmp3 * tmp23
    tl.store(out_ptr0 + (x3), tmp3, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
    tl.store(out_ptr2 + (x3), tmp11, xmask)
    tl.store(out_ptr3 + (x3), tmp19, xmask)
    tl.store(out_ptr4 + (x3), tmp22, xmask)
    tl.store(out_ptr5 + (x3), tmp24, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_4, (16, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [stacked_inputs], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        del primals_2
        # Topologically Sorted Source Nodes: [gates], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 16, 3, 3), (144, 9, 3, 1))
        buf3 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xi_1, xf_1, xo_1, xg_1, mul, mul_1, c, tanh_1, h], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, aten.add, aten.sigmoid_backward]
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1.run(buf1, primals_4, primals_5, buf3, buf2, buf4, buf5, buf7, buf6, 144, grid=grid(144), stream=stream0)
        del buf1
        del primals_4
    return (buf6, buf5, primals_3, primals_5, buf0, buf2, buf3, buf4, buf5, buf7, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, 8, 4, 4), (128, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
from torch.autograd import Variable


class ConvLSTMCell(nn.Module):

    def __init__(self, input_channels, hidden_channels, kernel_size, bias=True
        ):
        super(ConvLSTMCell, self).__init__()
        assert hidden_channels % 2 == 0
        self.input_channels = input_channels
        self.hidden_channels = hidden_channels
        self.bias = bias
        self.kernel_size = kernel_size
        self.padding = int((kernel_size - 1) / 2)
        self.Gates = nn.Conv2d(self.input_channels + self.hidden_channels, 
            4 * self.hidden_channels, self.kernel_size, 1, self.padding,
            bias=True)

    def forward(self, x, h, c):
        stacked_inputs = torch.cat((x, h), 1)
        gates = self.Gates(stacked_inputs)
        xi, xf, xo, xg = gates.chunk(4, 1)
        xi = torch.sigmoid(xi)
        xf = torch.sigmoid(xf)
        xo = torch.sigmoid(xo)
        xg = torch.tanh(xg)
        c = xf * c + xi * xg
        h = xo * torch.tanh(c)
        return h, c

    def init_hidden(self, batch_size, hidden, shape):
        return Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
            ), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1]))


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 3, 3])]


def get_init_inputs():
    return [[], {'input_channels': 4, 'hidden_channels': 4, 'kernel_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4,
    out_ptr5, xnumel, XBLOCK: tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 36
    x4 = xindex % 36
    x1 = xindex // 9 % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (72 + x4 + 144 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x4 + 144 * x2), xmask)
    tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (108 + x4 + 144 * x2), xmask)
    tmp9 = tl.load(in_ptr1 + (12 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (36 + x4 + 144 * x2), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp7 * tmp11
    tmp19 = tmp17 + tmp18
    tmp20 = 1.0
    tmp21 = tmp20 - tmp15
    tmp22 = tmp15 * tmp21
    tmp23 = libdevice.tanh(tmp19)
    tmp24 = tmp3 * tmp23
    tl.store(out_ptr0 + x3, tmp3, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)
    tl.store(out_ptr2 + x3, tmp11, xmask)
    tl.store(out_ptr3 + x3, tmp19, xmask)
    tl.store(out_ptr4 + x3, tmp22, xmask)
    tl.store(out_ptr5 + x3, tmp24, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 16, 3, 3), (144, 9, 3, 1))
        buf3 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1[grid(144)](
            buf1, primals_4, primals_5, buf3, buf2, buf4, buf5, buf7, buf6,
            144, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_4
    return buf6, buf5, primals_3, primals_5, buf0, buf2, buf3, buf4, buf5, buf7


class ConvLSTMCellNew(nn.Module):

    def __init__(self, input_channels, hidden_channels, kernel_size, bias=True
        ):
        super(ConvLSTMCellNew, self).__init__()
        assert hidden_channels % 2 == 0
        self.input_channels = input_channels
        self.hidden_channels = hidden_channels
        self.bias = bias
        self.kernel_size = kernel_size
        self.padding = int((kernel_size - 1) / 2)
        self.Gates = nn.Conv2d(self.input_channels + self.hidden_channels, 
            4 * self.hidden_channels, self.kernel_size, 1, self.padding,
            bias=True)

    def init_hidden(self, batch_size, hidden, shape):
        return Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
            ), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1]))

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.Gates.weight
        primals_4 = self.Gates.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
