# 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/rm/crmfe66pk7yrrmxujjf347i7knjkuxqdl4al5r7rzszjgyqvbzyx.py
# Topologically Sorted Source Nodes: [x_pad, x_relu], Original ATen: [aten.constant_pad_nd, aten.relu]
# Source node to ATen node mapping:
#   x_pad => constant_pad_nd
#   x_relu => relu
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [1, 1, 1, 1], 0.0), kwargs = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%constant_pad_nd,), kwargs = {})
triton_poi_fused_constant_pad_nd_relu_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_relu_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=[1024], 
    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_constant_pad_nd_relu_0', '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_constant_pad_nd_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 6
    x0 = xindex % 6
    x2 = (xindex // 36)
    x4 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x4), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/e6/ce6obqynchxhxolamqlbipmwc6fxzfi24eqt2ynzowrgkrkzg4nv.py
# Topologically Sorted Source Nodes: [x_out, x_relu_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_out => convolution
#   x_relu_3 => relu_3
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': [], '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_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 64
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kn/cknv3ppkmiow4nc3max3akdwyy3mx37cmh36ja4qiss5bnpfl5pr.py
# Topologically Sorted Source Nodes: [x_out, x_out_1, add, x_out_3, add_1, x_out_5, add_2], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   x_out => convolution
#   x_out_1 => convolution_1
#   x_out_3 => convolution_3
#   x_out_5 => convolution_5
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %convolution_1), kwargs = {})
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %convolution_3), kwargs = {})
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_8, %primals_9, [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 = (%add_1, %convolution_5), kwargs = {})
triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '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, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), None)
    tmp4 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x3), None)
    tmp8 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr5 + (x3), None)
    tmp12 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tl.store(in_out_ptr0 + (x3), tmp14, None)
''', 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, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    assert_size_stride(primals_4, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_pad, x_relu], Original ATen: [aten.constant_pad_nd, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_relu_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_out], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1))
        # Topologically Sorted Source Nodes: [x_out_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_out, x_relu_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf1, primals_3, buf3, 4096, grid=grid(4096), stream=stream0)
        # Topologically Sorted Source Nodes: [x_out_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_out_1, x_relu_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf2, primals_5, buf5, 4096, grid=grid(4096), stream=stream0)
        # Topologically Sorted Source Nodes: [x_out_5], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf7 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [x_out, x_out_1, add, x_out_3, add_1, x_out_5, add_2], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_2.run(buf7, primals_3, buf2, primals_5, buf4, primals_7, buf6, primals_9, 4096, grid=grid(4096), stream=stream0)
        del buf2
        del buf4
        del buf6
        del primals_3
        del primals_5
        del primals_7
        del primals_9
    return (buf7, primals_2, primals_4, primals_6, primals_8, buf0, buf3, buf5, )


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((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (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 math
import torch
import torch.utils.data
import torch
import torch.nn as nn


class Conv2dSame(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1):
        super(Conv2dSame, self).__init__()
        self.F = kernel_size
        self.S = stride
        self.D = dilation
        self.layer = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, dilation=dilation)

    def forward(self, x_in):
        _N, _C, H, W = x_in.shape
        H2 = math.ceil(H / self.S)
        W2 = math.ceil(W / self.S)
        Pr = (H2 - 1) * self.S + (self.F - 1) * self.D + 1 - H
        Pc = (W2 - 1) * self.S + (self.F - 1) * self.D + 1 - W
        x_pad = nn.ZeroPad2d((Pr // 2, Pr - Pr // 2, Pc // 2, Pc - Pc // 2))(
            x_in)
        x_relu = nn.ReLU()(x_pad)
        x_out = self.layer(x_relu)
        return x_out


class FeatureExtractionBlock(nn.Module):
    """Initialize the Resnet block

    A resnet block is a conv block with skip connections
    We construct a conv block with build_conv_block function,
    and implement skip connections in <forward> function.
    Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
    """

    def __init__(self, input_nc, ngf=64, norm_layer=nn.BatchNorm2d,
        use_dropout=False):
        """Construct a convolutional block.

        Parameters:
            dim (int)           -- the number of channels in the conv layer.
            norm_layer          -- normalization layer
            use_dropout (bool)  -- if use dropout layers.
            use_bias (bool)     -- if the conv layer uses bias or not

        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
        """
        super(FeatureExtractionBlock, self).__init__()
        self.C3R1 = Conv2dSame(input_nc, ngf, kernel_size=3)
        self.C1R1 = Conv2dSame(ngf, ngf, kernel_size=1)
        self.C3R2 = Conv2dSame(input_nc, ngf, kernel_size=3)
        self.C1R2 = Conv2dSame(ngf, ngf, kernel_size=1)

    def forward(self, input):
        return self.C3R1(input) + self.C3R2(input) + self.C1R1(self.C3R1(input)
            ) + self.C1R2(self.C3R2(input))


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


def get_init_inputs():
    return [[], {'input_nc': 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 import triton_helpers
import math
import torch.utils.data
import torch
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_constant_pad_nd_relu_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x2 = xindex // 36
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, None)
    tmp4 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x3, None)
    tmp8 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr5 + x3, None)
    tmp12 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tl.store(in_out_ptr0 + x3, tmp14, None)


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, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_9, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_relu_0[grid(576)](primals_1, buf0,
            576, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf2 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_relu_1[grid(4096)](buf1, primals_3,
            buf3, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_relu_1[grid(4096)](buf2, primals_5,
            buf5, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf7 = buf1
        del buf1
        triton_poi_fused_add_convolution_2[grid(4096)](buf7, primals_3,
            buf2, primals_5, buf4, primals_7, buf6, primals_9, 4096, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf2
        del buf4
        del buf6
        del primals_3
        del primals_5
        del primals_7
        del primals_9
    return buf7, primals_2, primals_4, primals_6, primals_8, buf0, buf3, buf5


class Conv2dSame(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1):
        super(Conv2dSame, self).__init__()
        self.F = kernel_size
        self.S = stride
        self.D = dilation
        self.layer = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, dilation=dilation)

    def forward(self, x_in):
        _N, _C, H, W = x_in.shape
        H2 = math.ceil(H / self.S)
        W2 = math.ceil(W / self.S)
        Pr = (H2 - 1) * self.S + (self.F - 1) * self.D + 1 - H
        Pc = (W2 - 1) * self.S + (self.F - 1) * self.D + 1 - W
        x_pad = nn.ZeroPad2d((Pr // 2, Pr - Pr // 2, Pc // 2, Pc - Pc // 2))(
            x_in)
        x_relu = nn.ReLU()(x_pad)
        x_out = self.layer(x_relu)
        return x_out


class FeatureExtractionBlockNew(nn.Module):
    """Initialize the Resnet block

    A resnet block is a conv block with skip connections
    We construct a conv block with build_conv_block function,
    and implement skip connections in <forward> function.
    Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
    """

    def __init__(self, input_nc, ngf=64, norm_layer=nn.BatchNorm2d,
        use_dropout=False):
        """Construct a convolutional block.

        Parameters:
            dim (int)           -- the number of channels in the conv layer.
            norm_layer          -- normalization layer
            use_dropout (bool)  -- if use dropout layers.
            use_bias (bool)     -- if the conv layer uses bias or not

        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
        """
        super(FeatureExtractionBlockNew, self).__init__()
        self.C3R1 = Conv2dSame(input_nc, ngf, kernel_size=3)
        self.C1R1 = Conv2dSame(ngf, ngf, kernel_size=1)
        self.C3R2 = Conv2dSame(input_nc, ngf, kernel_size=3)
        self.C1R2 = Conv2dSame(ngf, ngf, kernel_size=1)

    def forward(self, input_0):
        primals_2 = self.C3R1.layer.weight
        primals_3 = self.C3R1.layer.bias
        primals_6 = self.C1R1.layer.weight
        primals_5 = self.C1R1.layer.bias
        primals_4 = self.C3R2.layer.weight
        primals_7 = self.C3R2.layer.bias
        primals_8 = self.C1R2.layer.weight
        primals_9 = self.C1R2.layer.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]
