# 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/7u/c7udwysoza7dvec2fpqbwjuj5m3r5qfpatc65iwjfrakdawqtq7o.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   pad => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_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_reflection_pad2d_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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 8
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-2) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-2) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fj/cfjynfxcozwzl2uakbdos4egv5hpr6o7u36sthbkwh642p66zrlv.py
# Topologically Sorted Source Nodes: [conv2d, first, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   conv2d => convolution
#   first => relu
#   pad_1 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_1 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_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=[1024], 
    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_reflection_pad2d_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_reflection_pad2d_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 8
    x4 = (xindex // 64)
    x2 = (xindex // 64) % 3
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-2) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-2) + x1))))) + (16*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tn/ctnz5hsygejyczrzw25fgtrigocbwnjoz5vub6pttiojmoxevlc5.py
# Topologically Sorted Source Nodes: [conv2d, first, conv2d_1, second, conv2d_2, third], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d => convolution
#   conv2d_1 => convolution_1
#   conv2d_2 => convolution_2
#   first => relu
#   second => relu_1
#   third => relu_2
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
#   %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
#   %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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: '*fp32', 3: '*fp32', 4: '*i1', 5: '*i1', 6: '*i1', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (x3), xmask)
    tmp11 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp10, xmask)
    tl.store(out_ptr2 + (x3), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (3, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_3, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 768, grid=grid(768), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv2d], 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, 3, 4, 4), (48, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, first, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_1.run(buf1, primals_3, buf2, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 3, 4, 4), (48, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, second, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_1.run(buf3, primals_3, buf4, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 3, 4, 4), (48, 16, 4, 1))
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, first, conv2d_1, second, conv2d_2, third], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_2.run(buf6, primals_3, buf3, buf1, buf7, buf8, buf9, 192, grid=grid(192), stream=stream0)
        del buf1
        del buf3
        del primals_3
    return (buf6, primals_2, buf0, buf2, buf4, buf7, buf8, buf9, )


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, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((3, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.nn.functional as F


class NoiseNet(nn.Module):

    def __init__(self, channels=3, kernel_size=5):
        super(NoiseNet, self).__init__()
        self.kernel_size = kernel_size
        self.channels = channels
        to_pad = int((self.kernel_size - 1) / 2)
        self.padder = nn.ReflectionPad2d(to_pad).type(torch.FloatTensor)
        to_pad = 0
        self.convolver = nn.Conv2d(channels, channels, self.kernel_size, 1,
            padding=to_pad, bias=True).type(torch.FloatTensor)

    def forward(self, x):
        assert x.shape[1] == self.channels, (x.shape, self.channels)
        first = F.relu(self.convolver(self.padder(x)))
        second = F.relu(self.convolver(self.padder(first)))
        third = F.relu(self.convolver(self.padder(second)))
        assert x.shape == third.shape, (x.shape, third.shape)
        return third


def get_inputs():
    return [torch.rand([4, 3, 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 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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x4 = xindex // 64
    x2 = xindex // 64 % 3
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x4),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + x5, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + x3, xmask)
    tmp11 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)
    tl.store(out_ptr2 + x3, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (3, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_3, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(768)](primals_1, buf0, 768,
            XBLOCK=128, 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, 3, 4, 4), (48, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_1[grid(768)](buf1,
            primals_3, buf2, 768, XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 3, 4, 4), (48, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 3, 8, 8), (192, 64, 8, 1), torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_1[grid(768)](buf3,
            primals_3, buf4, 768, XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 3, 4, 4), (48, 16, 4, 1))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(192)](buf6,
            primals_3, buf3, buf1, buf7, buf8, buf9, 192, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        del buf3
        del primals_3
    return buf6, primals_2, buf0, buf2, buf4, buf7, buf8, buf9


class NoiseNetNew(nn.Module):

    def __init__(self, channels=3, kernel_size=5):
        super(NoiseNetNew, self).__init__()
        self.kernel_size = kernel_size
        self.channels = channels
        to_pad = int((self.kernel_size - 1) / 2)
        self.padder = nn.ReflectionPad2d(to_pad).type(torch.FloatTensor)
        to_pad = 0
        self.convolver = nn.Conv2d(channels, channels, self.kernel_size, 1,
            padding=to_pad, bias=True).type(torch.FloatTensor)

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