# 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_7/inductor_cache/pw/cpw5jgywzg5ntkknxkt5orxsrrr5zq7a6eoteboi3ba7zrcxj2p7.py
# Topologically Sorted Source Nodes: [f_k], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   f_k => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sc/cscr35vq6k7orwegv6m2bgtq2cvsy7mxgtqgqtl5kx4a6rtutl3v.py
# Topologically Sorted Source Nodes: [w], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   w => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 16), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_div_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/li/cliuki7kw34pevna3gnu3ihq5qfn3kvidywzkiifm6ybijryhkq6.py
# Topologically Sorted Source Nodes: [f_r_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   f_r_2 => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [f_v], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [f_k], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [f_q], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [f_k], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf3, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [f_q], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf4, primals_7, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf5 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf4, (4, 4, 16), (64, 16, 1), 0), out=buf5)
        buf6 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [f_v], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf6, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [w], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(buf7, 1024, grid=grid(1024), stream=stream0)
        buf8 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (4, 16, 16), (256, 1, 16), 0), reinterpret_tensor(buf6, (4, 16, 4), (64, 1, 16), 0), out=buf8)
        # Topologically Sorted Source Nodes: [f_r_2], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0), primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 1, 16, 4))
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [f_r_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf10, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (buf10, primals_1, primals_2, primals_4, primals_6, primals_8, reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0), buf7, reinterpret_tensor(buf6, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf3, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0), )


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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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


class RelationNonLocal(nn.Module):

    def __init__(self, C):
        super(RelationNonLocal, self).__init__()
        self.conv_fv = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fk = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fq = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fr = nn.Conv2d(C, C, kernel_size=1, stride=1)

    def forward(self, input_):
        N, C, H, W = input_.shape
        f_v = self.conv_fv(input_)
        f_k = self.conv_fk(input_)
        f_q = self.conv_fq(input_)
        f_k = f_k.reshape([N, C, H * W]).permute(0, 2, 1)
        f_q = f_q.reshape([N, C, H * W])
        w = torch.matmul(f_k, f_q) / (H * W)
        f_r = torch.matmul(w.permute(0, 2, 1), f_v.reshape([N, C, H * W]).
            permute(0, 2, 1)).permute(0, 2, 1)
        f_r = f_r.reshape(N, C, H, W)
        f_r = self.conv_fr(f_r)
        return f_r


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


def get_init_inputs():
    return [[], {'C': 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
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_div_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf3, primals_5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(256)](buf4, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf5 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 16, 4), (64, 1, 16),
            0), reinterpret_tensor(buf4, (4, 4, 16), (64, 16, 1), 0), out=buf5)
        buf6 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(256)](buf6, primals_3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf7 = buf5
        del buf5
        triton_poi_fused_div_1[grid(1024)](buf7, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (4, 16, 16), (256, 1, 
            16), 0), reinterpret_tensor(buf6, (4, 16, 4), (64, 1, 16), 0),
            out=buf8)
        buf9 = extern_kernels.convolution(reinterpret_tensor(buf8, (4, 4, 4,
            4), (64, 1, 16, 4), 0), primals_8, stride=(1, 1), padding=(0, 0
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 1, 16, 4))
        buf10 = buf9
        del buf9
        triton_poi_fused_convolution_2[grid(256)](buf10, primals_9, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
    return (buf10, primals_1, primals_2, primals_4, primals_6, primals_8,
        reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0), buf7,
        reinterpret_tensor(buf6, (4, 4, 16), (64, 16, 1), 0),
        reinterpret_tensor(buf3, (4, 4, 16), (64, 16, 1), 0),
        reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0))


class RelationNonLocalNew(nn.Module):

    def __init__(self, C):
        super(RelationNonLocalNew, self).__init__()
        self.conv_fv = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fk = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fq = nn.Conv2d(C, C, kernel_size=1, stride=1)
        self.conv_fr = nn.Conv2d(C, C, kernel_size=1, stride=1)

    def forward(self, input_0):
        primals_2 = self.conv_fv.weight
        primals_3 = self.conv_fv.bias
        primals_4 = self.conv_fk.weight
        primals_5 = self.conv_fk.bias
        primals_6 = self.conv_fq.weight
        primals_7 = self.conv_fq.bias
        primals_8 = self.conv_fr.weight
        primals_9 = self.conv_fr.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
