# 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_4/inductor_cache/ef/cefhdynsksovtheinxdd6apkd7critpzaqsxy5js2l3poicflqbx.py
# Topologically Sorted Source Nodes: [xy], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   xy => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%permute, %permute_1],), 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=[32], 
    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_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 2.0
    tmp8 = tmp6 < tmp7
    tmp9 = 0.6666666666666666
    tmp10 = tmp6 * tmp9
    tmp11 = -1.0
    tmp12 = tmp10 + tmp11
    tmp13 = 3 + ((-1)*x0)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 * tmp9
    tmp16 = 1.0
    tmp17 = tmp16 - tmp15
    tmp18 = tl.where(tmp8, tmp12, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp4, tmp18, tmp19)
    tmp21 = tmp0 >= tmp3
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = (-4) + x1
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp25 < tmp7
    tmp27 = tmp25 * tmp9
    tmp28 = tmp27 + tmp11
    tmp29 = 3 + ((-1)*((-4) + x1))
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp30 * tmp9
    tmp32 = tmp16 - tmp31
    tmp33 = tl.where(tmp26, tmp28, tmp32)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp21, tmp33, tmp34)
    tmp36 = tl.where(tmp4, tmp20, tmp35)
    tl.store(out_ptr0 + (x2), tmp36, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6z/c6zur3evwvj24ddb6kljl6dpfrtsx2ekjw76j2hzpurmutsijsra.py
# Topologically Sorted Source Nodes: [xy_fea], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   xy_fea => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%expand_2, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[128], 
    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_1', 'mutated_arg_names': [], '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_convolution_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ab/cabbhk2u3tivqjlxwgf2vo55v3h4clplmd6kgebydvpyi4fke72n.py
# Topologically Sorted Source Nodes: [xy_fea, xy_fea_1, out, out_1], Original ATen: [aten.convolution, aten.sin, aten.add]
# Source node to ATen node mapping:
#   out => convolution_1
#   out_1 => add_2
#   xy_fea => convolution
#   xy_fea_1 => sin
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%expand_2, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sin : [num_users=1] = call_function[target=torch.ops.aten.sin.default](args = (%convolution,), kwargs = {})
#   %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 = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sin, %convolution_1), kwargs = {})
triton_poi_fused_add_convolution_sin_2 = async_compile.triton('triton_poi_fused_add_convolution_sin_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_sin_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 4, '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_add_convolution_sin_2(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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')
    tmp4 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp5 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.sin(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, 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, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xy], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, 32, grid=grid(32), stream=stream0)
        buf1 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xy_fea], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf0, buf1, 128, grid=grid(128), stream=stream0)
        # Topologically Sorted Source Nodes: [xy_fea], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_2, 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, 1, 1), (4, 1, 1, 1))
        del buf1
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf4 = 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(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = buf2; del buf2  # reuse
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [xy_fea, xy_fea_1, out, out_1], Original ATen: [aten.convolution, aten.sin, aten.add]
        triton_poi_fused_add_convolution_sin_2.run(buf3, buf5, primals_3, primals_5, 16, grid=grid(16), stream=stream0)
        del primals_3
        del primals_5
    return (buf5, primals_1, primals_2, primals_4, buf0, buf3, )


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, 2, 4, 4), (32, 16, 4, 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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (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


class SinAct(nn.Module):

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

    def forward(self, x):
        return torch.sin(x)


class CoordConvSinAct(nn.Module):
    """
  Source: https://github.com/mkocabas/CoordConv-pytorch/blob/master/CoordConv.py
  """

    def __init__(self, in_channels, out_channels, channels_per_group=16, **
        kwargs):
        super().__init__()
        self.coord_conv = nn.Conv2d(2, out_channels, **kwargs)
        self.sin_act = SinAct()
        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
        pass

    def forward(self, input):
        batch, _, H, W = input.shape
        x, y = torch.meshgrid(torch.linspace(-1, 1, W, device=input.device),
            torch.linspace(-1, 1, H, device=input.device))
        x = x.T
        y = y.T
        xy = torch.stack((x, y), dim=0)
        xy = xy.expand((batch, -1, -1, -1))
        xy_fea = self.coord_conv(xy)
        xy_fea = self.sin_act(xy_fea)
        out = self.conv(input)
        out = xy_fea + out
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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 math as tl_math
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


@triton.jit
def triton_poi_fused_stack_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 2.0
    tmp8 = tmp6 < tmp7
    tmp9 = 0.6666666666666666
    tmp10 = tmp6 * tmp9
    tmp11 = -1.0
    tmp12 = tmp10 + tmp11
    tmp13 = 3 + -1 * x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 * tmp9
    tmp16 = 1.0
    tmp17 = tmp16 - tmp15
    tmp18 = tl.where(tmp8, tmp12, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp4, tmp18, tmp19)
    tmp21 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp24 = -4 + x1
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp25 < tmp7
    tmp27 = tmp25 * tmp9
    tmp28 = tmp27 + tmp11
    tmp29 = 3 + -1 * (-4 + x1)
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp30 * tmp9
    tmp32 = tmp16 - tmp31
    tmp33 = tl.where(tmp26, tmp28, tmp32)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp21, tmp33, tmp34)
    tmp36 = tl.where(tmp4, tmp20, tmp35)
    tl.store(out_ptr0 + x2, tmp36, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_convolution_sin_2(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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')
    tmp4 = tl.load(in_out_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.sin(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, 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, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(32)](buf0, 32, XBLOCK=32, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(128)](buf0, buf1, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_2, 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, 1, 1), (4, 1, 1, 1))
        del buf1
        buf4 = 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(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = buf2
        del buf2
        buf5 = buf4
        del buf4
        triton_poi_fused_add_convolution_sin_2[grid(16)](buf3, buf5,
            primals_3, primals_5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        del primals_5
    return buf5, primals_1, primals_2, primals_4, buf0, buf3


class SinAct(nn.Module):

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

    def forward(self, x):
        return torch.sin(x)


class CoordConvSinActNew(nn.Module):
    """
  Source: https://github.com/mkocabas/CoordConv-pytorch/blob/master/CoordConv.py
  """

    def __init__(self, in_channels, out_channels, channels_per_group=16, **
        kwargs):
        super().__init__()
        self.coord_conv = nn.Conv2d(2, out_channels, **kwargs)
        self.sin_act = SinAct()
        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
        pass

    def forward(self, input_0):
        primals_2 = self.coord_conv.weight
        primals_3 = self.coord_conv.bias
        primals_1 = self.conv.weight
        primals_5 = self.conv.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
