# AOT ID: ['0_inference']
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_6/inductor_cache/io/ciob4kb36gg4wx77qlmocg7jy3y7sntoa4fvgm3oato4xhh5cpe4.py
# Topologically Sorted Source Nodes: [similarities], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   similarities => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %mul_1, %mul_2, %mul_3],), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', '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_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    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), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (4*x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.load(in_ptr1 + (1 + (4*x0)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = tl.load(in_ptr1 + (2 + (4*x0)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tl.load(in_ptr1 + (3 + (4*x0)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = 1e-10
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = float("inf")
    tmp20 = triton_helpers.minimum(tmp18, tmp19)
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp5 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tmp0 >= tmp3
    tmp26 = tl.full([1], 8, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr2 + ((-4) + x0), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tl.load(in_ptr1 + (16 + (4*((-4) + x0))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 * tmp30
    tmp32 = tl.load(in_ptr1 + (17 + (4*((-4) + x0))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tl.load(in_ptr1 + (18 + (4*((-4) + x0))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = tl.load(in_ptr1 + (19 + (4*((-4) + x0))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp17)
    tmp42 = triton_helpers.minimum(tmp41, tmp19)
    tmp43 = libdevice.rsqrt(tmp42)
    tmp44 = tmp29 * tmp43
    tmp45 = tl.full(tmp44.shape, 0.0, tmp44.dtype)
    tmp46 = tl.where(tmp28, tmp44, tmp45)
    tmp47 = tmp0 >= tmp26
    tmp48 = tl.full([1], 12, tl.int64)
    tmp49 = tmp0 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tl.load(in_ptr3 + ((-8) + x0), tmp50 & xmask, eviction_policy='evict_last', other=0.0)
    tmp52 = tl.load(in_ptr1 + (32 + (4*((-8) + x0))), tmp50 & xmask, eviction_policy='evict_last', other=0.0)
    tmp53 = tmp52 * tmp52
    tmp54 = tl.load(in_ptr1 + (33 + (4*((-8) + x0))), tmp50 & xmask, eviction_policy='evict_last', other=0.0)
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tl.load(in_ptr1 + (34 + (4*((-8) + x0))), tmp50 & xmask, eviction_policy='evict_last', other=0.0)
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tl.load(in_ptr1 + (35 + (4*((-8) + x0))), tmp50 & xmask, eviction_policy='evict_last', other=0.0)
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = triton_helpers.maximum(tmp62, tmp17)
    tmp64 = triton_helpers.minimum(tmp63, tmp19)
    tmp65 = libdevice.rsqrt(tmp64)
    tmp66 = tmp51 * tmp65
    tmp67 = tl.full(tmp66.shape, 0.0, tmp66.dtype)
    tmp68 = tl.where(tmp50, tmp66, tmp67)
    tmp69 = tmp0 >= tmp48
    tmp70 = tl.full([1], 16, tl.int64)
    tmp71 = tmp0 < tmp70
    tmp72 = tl.load(in_ptr4 + ((-12) + x0), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp73 = tl.load(in_ptr1 + (48 + (4*((-12) + x0))), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp74 = tmp73 * tmp73
    tmp75 = tl.load(in_ptr1 + (49 + (4*((-12) + x0))), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp76 = tmp75 * tmp75
    tmp77 = tmp74 + tmp76
    tmp78 = tl.load(in_ptr1 + (50 + (4*((-12) + x0))), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp79 = tmp78 * tmp78
    tmp80 = tmp77 + tmp79
    tmp81 = tl.load(in_ptr1 + (51 + (4*((-12) + x0))), tmp69 & xmask, eviction_policy='evict_last', other=0.0)
    tmp82 = tmp81 * tmp81
    tmp83 = tmp80 + tmp82
    tmp84 = triton_helpers.maximum(tmp83, tmp17)
    tmp85 = triton_helpers.minimum(tmp84, tmp19)
    tmp86 = libdevice.rsqrt(tmp85)
    tmp87 = tmp72 * tmp86
    tmp88 = tl.full(tmp87.shape, 0.0, tmp87.dtype)
    tmp89 = tl.where(tmp69, tmp87, tmp88)
    tmp90 = tl.where(tmp50, tmp68, tmp89)
    tmp91 = tl.where(tmp28, tmp46, tmp90)
    tmp92 = tl.where(tmp4, tmp24, tmp91)
    tl.store(out_ptr0 + (x0), tmp92, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_product], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_product_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 16), out=buf1)
        buf2 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_product_4], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 32), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_product_6], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 48), out=buf3)
        del arg1_1
        buf4 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [similarities], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, arg0_1, buf1, buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del buf0
        del buf1
        del buf2
        del buf3
    return (reinterpret_tensor(buf4, (4, 4), (4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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.utils.data
import torch.nn as nn


class PairCosineSim(nn.Module):

    def __init__(self):
        super(PairCosineSim, self).__init__()

    def forward(self, supports, target):
        """
        Calculates pairwise cosine similarity of support sets with target sample.

        :param supports: The embeddings of the support set samples, tensor of shape [batch_size, sequence_length, input_size]
        :param targets: The embedding of the target sample, tensor of shape [batch_size, input_size] -> [batch_size, sequence_length, input_size]

        :return: Tensor with cosine similarities of shape [batch_size, target_size, support_size]
        """
        eps = 1e-10
        similarities = []
        for support_image in supports:
            sum_support = torch.sum(torch.pow(support_image, 2), 1)
            support_magnitude = sum_support.clamp(eps, float('inf')).rsqrt()
            target_unsqueeze = target.unsqueeze(1)
            support_image_unsqueeze = support_image.unsqueeze(2)
            dot_product = target_unsqueeze.bmm(support_image_unsqueeze)
            dot_product = dot_product.squeeze()
            cos_sim = dot_product * support_magnitude
            similarities.append(cos_sim)
        similarities = torch.stack(similarities)
        return similarities


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


def get_init_inputs():
    return [[], {}]

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + 4 * x0, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tl.load(in_ptr1 + (3 + 4 * x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = 1e-10
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = float('inf')
    tmp20 = triton_helpers.minimum(tmp18, tmp19)
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp5 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tmp0 >= tmp3
    tmp26 = tl.full([1], 8, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr2 + (-4 + x0), tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp30 = tl.load(in_ptr1 + (16 + 4 * (-4 + x0)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 * tmp30
    tmp32 = tl.load(in_ptr1 + (17 + 4 * (-4 + x0)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tl.load(in_ptr1 + (18 + 4 * (-4 + x0)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = tl.load(in_ptr1 + (19 + 4 * (-4 + x0)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp17)
    tmp42 = triton_helpers.minimum(tmp41, tmp19)
    tmp43 = libdevice.rsqrt(tmp42)
    tmp44 = tmp29 * tmp43
    tmp45 = tl.full(tmp44.shape, 0.0, tmp44.dtype)
    tmp46 = tl.where(tmp28, tmp44, tmp45)
    tmp47 = tmp0 >= tmp26
    tmp48 = tl.full([1], 12, tl.int64)
    tmp49 = tmp0 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tl.load(in_ptr3 + (-8 + x0), tmp50 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp52 = tl.load(in_ptr1 + (32 + 4 * (-8 + x0)), tmp50 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp53 = tmp52 * tmp52
    tmp54 = tl.load(in_ptr1 + (33 + 4 * (-8 + x0)), tmp50 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tl.load(in_ptr1 + (34 + 4 * (-8 + x0)), tmp50 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tl.load(in_ptr1 + (35 + 4 * (-8 + x0)), tmp50 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = triton_helpers.maximum(tmp62, tmp17)
    tmp64 = triton_helpers.minimum(tmp63, tmp19)
    tmp65 = libdevice.rsqrt(tmp64)
    tmp66 = tmp51 * tmp65
    tmp67 = tl.full(tmp66.shape, 0.0, tmp66.dtype)
    tmp68 = tl.where(tmp50, tmp66, tmp67)
    tmp69 = tmp0 >= tmp48
    tl.full([1], 16, tl.int64)
    tmp72 = tl.load(in_ptr4 + (-12 + x0), tmp69 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp73 = tl.load(in_ptr1 + (48 + 4 * (-12 + x0)), tmp69 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp74 = tmp73 * tmp73
    tmp75 = tl.load(in_ptr1 + (49 + 4 * (-12 + x0)), tmp69 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp76 = tmp75 * tmp75
    tmp77 = tmp74 + tmp76
    tmp78 = tl.load(in_ptr1 + (50 + 4 * (-12 + x0)), tmp69 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp79 = tmp78 * tmp78
    tmp80 = tmp77 + tmp79
    tmp81 = tl.load(in_ptr1 + (51 + 4 * (-12 + x0)), tmp69 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp82 = tmp81 * tmp81
    tmp83 = tmp80 + tmp82
    tmp84 = triton_helpers.maximum(tmp83, tmp17)
    tmp85 = triton_helpers.minimum(tmp84, tmp19)
    tmp86 = libdevice.rsqrt(tmp85)
    tmp87 = tmp72 * tmp86
    tmp88 = tl.full(tmp87.shape, 0.0, tmp87.dtype)
    tmp89 = tl.where(tmp69, tmp87, tmp88)
    tmp90 = tl.where(tmp50, tmp68, tmp89)
    tmp91 = tl.where(tmp28, tmp46, tmp90)
    tmp92 = tl.where(tmp4, tmp24, tmp91)
    tl.store(out_ptr0 + x0, tmp92, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 16), out=buf1)
        buf2 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 32), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 48), out=buf3)
        del arg1_1
        buf4 = empty_strided_cuda((16,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(16)](buf0, arg0_1, buf1, buf2, buf3,
            buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del buf0
        del buf1
        del buf2
        del buf3
    return reinterpret_tensor(buf4, (4, 4), (4, 1), 0),


class PairCosineSimNew(nn.Module):

    def __init__(self):
        super(PairCosineSimNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
