# 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_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out => _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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 73728
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   out_3 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {})
#   %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/iw/ciwouyfqxfi5suuzcagn3bbdbaulgyw4bpqnsdjlizmkc2oz2muq.py
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_1 => convolution
#   out_2 => relu
#   out_3 => _unsafe_index_2
#   out_4 => _unsafe_index_3, _unsafe_index_4
# 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, %unsqueeze, %convert_element_type_1]), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 102400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 10) % 10
    x0 = xindex % 10
    x4 = (xindex // 100)
    x2 = (xindex // 100) % 256
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + (4*tmp4) + (16*x4)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/x6/cx6kpvwny3lwf4b75yjzmpojqlofwsrfkk23uf3lterlix2wg3ah.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_5 => convolution_1
#   out_6 => relu_1
#   out_7 => _unsafe_index_5, _unsafe_index_6
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [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 = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {})
#   %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', '''
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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 102400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 10
    x1 = (xindex // 10) % 10
    x4 = (xindex // 100)
    x2 = (xindex // 100) % 256
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64*x4)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), 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 + (x5), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py
# Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   out_16 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_12, mul_4, mul_5
# Graph fragment:
#   %iota_12 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {})
#   %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', '''
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: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy_add_arange_mul_4(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 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/u4/cu42vug3hzqlursadot6js3gerux56tz7joz5blmrj6a5xowpyd6.py
# Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_14 => convolution_4
#   out_15 => relu_4
#   out_16 => _unsafe_index_11
#   out_17 => _unsafe_index_12, _unsafe_index_13
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
#   %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {})
#   %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {})
#   %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', '''
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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 18) % 18
    x0 = xindex % 18
    x4 = (xindex // 324)
    x2 = (xindex // 324) % 128
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + (8*tmp4) + (64*x4)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/lz/clznsh6fdhksacfsunvntibvsjaoxwmok2tqs5avvjtlb7uojj5w.py
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_18 => convolution_5
#   out_19 => relu_5
#   out_20 => _unsafe_index_14, _unsafe_index_15
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
#   %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {})
#   %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', '''
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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = (xindex // 18) % 18
    x4 = (xindex // 324)
    x2 = (xindex // 324) % 128
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x4)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), 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 + (x5), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/6w/c6wyyvxovkkzfk6k64bfon5db5xfxiywvm7oionlfmjjhdvc6ugb.py
# Topologically Sorted Source Nodes: [out_23], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   out_23 => add_8, add_9, convert_element_type_8, convert_element_type_9, iota_18, mul_8, mul_9
# Graph fragment:
#   %iota_18 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_18, 1), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {})
#   %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {})
#   %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_7 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_7', '''
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: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/74/c74eexru54jzdd7so47hr6vfjhuchik3ect6ywzjtozle2ccoanv.py
# Topologically Sorted Source Nodes: [out_21, out_22, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_21 => convolution_6
#   out_22 => relu_6
#   out_23 => _unsafe_index_16
#   out_24 => _unsafe_index_17, _unsafe_index_18
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
#   %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_6, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {})
#   %_unsafe_index_17 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, %sub_29, None]), kwargs = {})
#   %_unsafe_index_18 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_17, [None, None, None, %sub_29]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', '''
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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 34) % 34
    x0 = xindex % 34
    x4 = (xindex // 1156)
    x2 = (xindex // 1156) % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + (16*tmp4) + (256*x4)), xmask, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x7), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/hz/chz63phj47p2gjm5xtlsmszcrnfpp4xsj7dae5glkrxk5zs7hyto.py
# Topologically Sorted Source Nodes: [out_25, out_26, out_27], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   out_25 => convolution_7
#   out_26 => relu_7
#   out_27 => _unsafe_index_19, _unsafe_index_20
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
#   %_unsafe_index_19 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_7, [None, None, %sub_29, None]), kwargs = {})
#   %_unsafe_index_20 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_19, [None, None, None, %sub_29]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', '''
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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 34
    x1 = (xindex // 34) % 34
    x4 = (xindex // 1156)
    x2 = (xindex // 1156) % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (1023 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024*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_1/inductor_cache/hn/chnibi7gwebi7kg4g3szjbyckwlp74j2tqdbjdrt6nzxxpvjzwhu.py
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out_28 => convolution_8
# Graph fragment:
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_20, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_convolution_10', '''
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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/5o/c5ojoadwtfg74b52gh7y53jwhw2ktpi5q7bj2iprlawfjiexyy3r.py
# Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_25 => convolution_7
#   out_26 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
#   %le_18 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', '''
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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/aa/caa4f5re2hltkmtlkj3n4yzdxyuaw5kf6spnc6g2phrpe6faiy4x.py
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_21 => convolution_6
#   out_22 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
#   %le_37 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_12', '''
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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/jr/cjrwyczsgjgyy6qosgum4wgnyb2w3ihpqvja6fteyhda2zxoh5ot.py
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_18 => convolution_5
#   out_19 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
#   %le_56 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_13 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_13', '''
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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 128
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/sz/cszsc7yevnmc6ugclb5yovyvzqbxqknvq6uzgneygczjmkvgtczc.py
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_14 => convolution_4
#   out_15 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
#   %le_75 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', '''
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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/rf/crftfxpfxphanidbjn5jncdcv65uaywlzsfsv5jzcsmdxwvjvf2c.py
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_11 => convolution_3
#   out_12 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [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_3,), kwargs = {})
#   %le_94 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', '''
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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ia/cianovbanealq7vsk3q7ox66wiuiaoeqjzpupak5lcrtiilg2ewl.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_1 => convolution
#   out_2 => relu
# 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 = {})
#   %le_151 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', '''
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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, 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, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args
    args.clear()
    assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_3, (256, ), (1, ))
    assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (256, ), (1, ))
    assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (256, ), (1, ))
    assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_11, (128, ), (1, ))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128, ), (1, ))
    assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64, ), (1, ))
    assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_19, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [out_1], 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, 256, 4, 4), (4096, 16, 4, 1))
        buf2 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0)
        buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 102400, grid=grid(102400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, 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, 256, 8, 8), (16384, 64, 8, 1))
        buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 102400, grid=grid(102400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 102400, grid=grid(102400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 102400, grid=grid(102400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf11 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_4.run(buf11, 16, grid=grid(16), stream=stream0)
        buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf11, buf10, primals_11, buf12, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.convolution]
        buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf13, primals_13, buf14, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [out_21], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf16 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [out_23], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_7.run(buf16, 32, grid=grid(32), stream=stream0)
        buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_21, out_22, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8.run(buf16, buf15, primals_15, buf17, 295936, grid=grid(295936), stream=stream0)
        # Topologically Sorted Source Nodes: [out_25], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_25, out_26, out_27], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf18, primals_17, buf19, 295936, grid=grid(295936), stream=stream0)
        # Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf21, primals_19, 12288, grid=grid(12288), stream=stream0)
        del primals_19
        buf22 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_11.run(buf18, primals_17, buf22, 262144, grid=grid(262144), stream=stream0)
        del buf18
        del primals_17
        buf23 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_12.run(buf15, primals_15, buf23, 65536, grid=grid(65536), stream=stream0)
        del buf15
        del primals_15
        buf24 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_13.run(buf13, primals_13, buf24, 131072, grid=grid(131072), stream=stream0)
        del buf13
        del primals_13
        buf25 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_14.run(buf10, primals_11, buf25, 32768, grid=grid(32768), stream=stream0)
        del buf10
        del primals_11
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_15.run(buf8, primals_9, buf26, 65536, grid=grid(65536), stream=stream0)
        del buf8
        del primals_9
        buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_15.run(buf6, primals_7, buf27, 65536, grid=grid(65536), stream=stream0)
        del buf6
        del primals_7
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_15.run(buf4, primals_5, buf28, 65536, grid=grid(65536), stream=stream0)
        del buf4
        del primals_5
        buf29 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_16.run(buf1, primals_3, buf29, 16384, grid=grid(16384), stream=stream0)
        del buf1
        del primals_3
    return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22, buf23, buf24, buf25, buf26, buf27, buf28, buf29, )


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, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((3, ), (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, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19])
    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
from torch import nn


class decoder4(nn.Module):

    def __init__(self):
        super(decoder4, self).__init__()
        self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
        self.relu11 = nn.ReLU(inplace=True)
        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu12 = nn.ReLU(inplace=True)
        self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu13 = nn.ReLU(inplace=True)
        self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu14 = nn.ReLU(inplace=True)
        self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
        self.relu15 = nn.ReLU(inplace=True)
        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
        self.relu16 = nn.ReLU(inplace=True)
        self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
        self.relu17 = nn.ReLU(inplace=True)
        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
        self.relu18 = nn.ReLU(inplace=True)
        self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)

    def forward(self, x):
        out = self.reflecPad11(x)
        out = self.conv11(out)
        out = self.relu11(out)
        out = self.unpool(out)
        out = self.reflecPad12(out)
        out = self.conv12(out)
        out = self.relu12(out)
        out = self.reflecPad13(out)
        out = self.conv13(out)
        out = self.relu13(out)
        out = self.reflecPad14(out)
        out = self.conv14(out)
        out = self.relu14(out)
        out = self.reflecPad15(out)
        out = self.conv15(out)
        out = self.relu15(out)
        out = self.unpool2(out)
        out = self.reflecPad16(out)
        out = self.conv16(out)
        out = self.relu16(out)
        out = self.reflecPad17(out)
        out = self.conv17(out)
        out = self.relu17(out)
        out = self.unpool3(out)
        out = self.reflecPad18(out)
        out = self.conv18(out)
        out = self.relu18(out)
        out = self.reflecPad19(out)
        out = self.conv19(out)
        return out


def get_inputs():
    return [torch.rand([4, 512, 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
from torch import 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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 10 % 10
    x0 = xindex % 10
    x4 = xindex // 100
    x2 = xindex // 100 % 256
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1
        ))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0
        ))), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x7, tmp13, None)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(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)
    x0 = xindex % 10
    x1 = xindex // 10 % 10
    x4 = xindex // 100
    x2 = xindex // 100 % 256
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
        x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, 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 + x5, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(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 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 18 % 18
    x0 = xindex % 18
    x4 = xindex // 324
    x2 = xindex // 324 % 128
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x1))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0))), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x7, tmp13, None)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(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)
    x0 = xindex % 18
    x1 = xindex // 18 % 18
    x4 = xindex // 324
    x2 = xindex // 324 % 128
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, 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 + x5, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 34 % 34
    x0 = xindex % 34
    x4 = xindex // 1156
    x2 = xindex // 1156 % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x1))), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0))), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), xmask,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x7, tmp13, xmask)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 34
    x1 = xindex // 34 % 34
    x4 = xindex // 1156
    x2 = xindex // 1156 % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * 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_10(in_out_ptr0, in_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 // 1024 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(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 // 1024 % 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_12(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 // 256 % 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_13(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 // 256 % 128
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(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 // 64 % 128
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(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 // 64 % 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(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 % 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19) = args
    args.clear()
    assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (256,), (1,))
    assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64,), (1,))
    assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_19, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0, 
            73728, XBLOCK=1024, 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, 256, 4, 4), (4096, 16, 4, 1))
        buf2 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK
            =8, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid
            (102400)](buf2, buf1, primals_3, buf3, 102400, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, 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, 256, 8, 8), (16384, 64, 8, 1))
        buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf4
            , primals_5, buf5, 102400, XBLOCK=512, num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf6
            , primals_7, buf7, 102400, XBLOCK=512, num_warps=8, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf8
            , primals_9, buf9, 102400, XBLOCK=512, num_warps=8, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf11 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf11, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid
            (165888)](buf11, buf10, primals_11, buf12, 165888, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(165888)](
            buf13, primals_13, buf14, 165888, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf16 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf16, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8[grid
            (295936)](buf16, buf15, primals_15, buf17, 295936, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(295936)](
            buf18, primals_17, buf19, 295936, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_10[grid(12288)](buf21, primals_19, 
            12288, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_19
        buf22 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_11[grid(262144)](
            buf18, primals_17, buf22, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf18
        del primals_17
        buf23 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_12[grid(65536)](
            buf15, primals_15, buf23, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf15
        del primals_15
        buf24 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_13[grid(131072)](
            buf13, primals_13, buf24, 131072, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf13
        del primals_13
        buf25 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_14[grid(32768)](
            buf10, primals_11, buf25, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf10
        del primals_11
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
            buf8, primals_9, buf26, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf8
        del primals_9
        buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
            buf6, primals_7, buf27, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf6
        del primals_7
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
            buf4, primals_5, buf28, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf4
        del primals_5
        buf29 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_16[grid(16384)](
            buf1, primals_3, buf29, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf1
        del primals_3
    return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10,
        primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3,
        buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22,
        buf23, buf24, buf25, buf26, buf27, buf28, buf29)


class decoder4New(nn.Module):

    def __init__(self):
        super(decoder4New, self).__init__()
        self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
        self.relu11 = nn.ReLU(inplace=True)
        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu12 = nn.ReLU(inplace=True)
        self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu13 = nn.ReLU(inplace=True)
        self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
        self.relu14 = nn.ReLU(inplace=True)
        self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
        self.relu15 = nn.ReLU(inplace=True)
        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
        self.relu16 = nn.ReLU(inplace=True)
        self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
        self.relu17 = nn.ReLU(inplace=True)
        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
        self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
        self.relu18 = nn.ReLU(inplace=True)
        self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
        self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)

    def forward(self, input_0):
        primals_2 = self.conv11.weight
        primals_3 = self.conv11.bias
        primals_4 = self.conv12.weight
        primals_5 = self.conv12.bias
        primals_6 = self.conv13.weight
        primals_7 = self.conv13.bias
        primals_8 = self.conv14.weight
        primals_9 = self.conv14.bias
        primals_10 = self.conv15.weight
        primals_11 = self.conv15.bias
        primals_12 = self.conv16.weight
        primals_13 = self.conv16.bias
        primals_14 = self.conv17.weight
        primals_15 = self.conv17.bias
        primals_16 = self.conv18.weight
        primals_17 = self.conv18.bias
        primals_18 = self.conv19.weight
        primals_19 = self.conv19.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,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19])
        return output[0]
