# 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_8/inductor_cache/xi/cxi3ssslzv45liamqvbt6decmfms5gkzbjn7dtainfaa436qkyw3.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x => _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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 62208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 72
    x1 = (xindex // 72) % 72
    x2 = (xindex // 5184)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-4) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-4) + x1))))) + (4096*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/zp/czpuakvx3zciuzfmemejrltenkqbzqirfyy2fnfbmrorwkdndz6e.py
# Topologically Sorted Source Nodes: [conv2d, instance_norm], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   conv2d => convolution
#   instance_norm => add, rsqrt, var_mean
# Graph fragment:
#   %convolution : [num_users=2] = 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 = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_red_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_red_fused__native_batch_norm_legit_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[128, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_convolution_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 32
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + (4096*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce(
            tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0
        )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + (4096*x3)), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(
        tmp4_mean, tmp4_m2, tmp4_weight, 1
    )
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/in/ciny2bql3sygecchlvr6rxw73jnhl7dgi3s5w2g2fefaoug53zzz.py
# Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   instance_norm => repeat
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_4, [4]), kwargs = {})
triton_poi_fused_repeat_2 = async_compile.triton('triton_poi_fused_repeat_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 % 32), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ii/ciidusl6utkne6h3zmwx3jccsnttcsdc42mtp3vanldcnxv4y7ov.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x_1 => relu
#   x_2 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_6, None]), kwargs = {})
#   %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_6]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 66
    x1 = (xindex // 66) % 66
    x2 = (xindex // 4356)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (4096*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/si/csiohvngy3nd4p3av6rdkonvlcuns665sjcyq5ggukrhfwpso4ay.py
# Topologically Sorted Source Nodes: [conv2d_1, instance_norm_1], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   instance_norm_1 => add_2, rsqrt_1, var_mean_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_4 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_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.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_4', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_4(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 256
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + (1024*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 1024.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (1024*x3)), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/bo/cbop6byfkkzzjktajzua3ovnpvhy32nxb7dbv364jfeaxunlv7bo.py
# Topologically Sorted Source Nodes: [instance_norm_1], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   instance_norm_1 => repeat_2
# Graph fragment:
#   %repeat_2 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_8, [4]), kwargs = {})
triton_poi_fused_repeat_5 = async_compile.triton('triton_poi_fused_repeat_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 % 64), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/k6/ck6ljtglelyaqir7indwg3cp4wwudzqtlaof4xfdlyasdzhka7z5.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x_3 => relu_1
#   x_4 => _unsafe_index_4, _unsafe_index_5
# Graph fragment:
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_11, None]), kwargs = {})
#   %_unsafe_index_5 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_4, [None, None, None, %sub_11]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
    x2 = (xindex // 1156)
    x3 = 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*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/b3/cb3i36nfih3ah5aifo46hyitngbbqmrioka4h7sa3nz2vzd5toin.py
# Topologically Sorted Source Nodes: [conv2d_2, instance_norm_2, x_5], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   instance_norm_2 => add_4, repeat_4, repeat_5, rsqrt_2, var_mean_2
#   x_5 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_10, %primals_11, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %repeat_4 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_12, [4]), kwargs = {})
#   %repeat_5 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_13, [4]), kwargs = {})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_4, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_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.persistent_reduction(
    size_hints=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x0 % 128), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 % 128), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_out_ptr0 + (r3 + (256*x0)), None)
    tmp3 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 256, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = 256.0
    tmp19 = tmp17 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp4 - tmp12
    tmp24 = tmp23 * tmp22
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp1
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tl.store(out_ptr0 + (x0), tmp0, None)
    tl.store(out_ptr1 + (x0), tmp1, None)
    tl.store(in_out_ptr0 + (r3 + (256*x0)), tmp4, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp22, None)
    tl.store(out_ptr3 + (r3 + (256*x0)), tmp28, None)
    tl.store(out_ptr2 + (x0), tmp12, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/st/cstfzn4z33vdn3t4r76kkdoe3fox63ob7zbuq5lr4e2aj2wo3cfw.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x_6 => _unsafe_index_6, _unsafe_index_7
# Graph fragment:
#   %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %sub_16, None]), kwargs = {})
#   %_unsafe_index_7 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_6, [None, None, None, %sub_16]), kwargs = {})
triton_poi_fused_reflection_pad2d_8 = async_compile.triton('triton_poi_fused_reflection_pad2d_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_8(in_ptr0, 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
    x2 = (xindex // 324)
    x3 = 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*x2)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/e2/ce2xxpjelyctnuhefg5fuzcvwpa544akythto7ai5tgzpkjchqwu.py
# Topologically Sorted Source Nodes: [conv2d_3, instance_norm_3], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   instance_norm_3 => add_6, rsqrt_3, var_mean_3
# Graph fragment:
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_7, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_3 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_6, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_6, 1e-05), kwargs = {})
#   %rsqrt_3 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_6,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_9 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_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.persistent_reduction(
    size_hints=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_9', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_9(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/df/cdfz5yaux6hd3x6u7ywjjuon3rgwzpj6jchxqf6fmzsftmjj7luu.py
# Topologically Sorted Source Nodes: [instance_norm_3], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   instance_norm_3 => repeat_6
# Graph fragment:
#   %repeat_6 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_16, [4]), kwargs = {})
triton_poi_fused_repeat_10 = async_compile.triton('triton_poi_fused_repeat_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_10(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 % 128), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/72/c72anaicoavbg3ypt27amkloa7kkqjupcqqr7kifcj4pxrdujccb.py
# Topologically Sorted Source Nodes: [x_7, x_8], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x_7 => relu_3
#   x_8 => _unsafe_index_8, _unsafe_index_9
# Graph fragment:
#   %relu_3 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_7,), kwargs = {})
#   %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_3, [None, None, %sub_16, None]), kwargs = {})
#   %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_16]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_11 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
    x2 = (xindex // 324)
    x3 = 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*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/gy/cgyk5fdbnu7tb64ewxqskjmwazfxpw7knxwr433ixolir3zwsqxm.py
# Topologically Sorted Source Nodes: [conv2d_4, x_9, x_10], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   x_10 => add_10
#   x_9 => add_8, repeat_8, rsqrt_4, var_mean_4
# Graph fragment:
#   %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %repeat_8 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_20, [4]), kwargs = {})
#   %var_mean_4 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_8, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
#   %rsqrt_4 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_8,), kwargs = {})
#   %add_10 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_2, %view_9), kwargs = {})
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12 = async_compile.triton('triton_per_fused__native_batch_norm_legit_add_convolution_repeat_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.persistent_reduction(
    size_hints=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 5, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x0 % 128), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + (256*x0)), None)
    tmp2 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_out_ptr1 + (r3 + (256*x0)), None)
    tmp26 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tmp9 = tl.full([1], 256, tl.int32)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp8 / tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp18 = tmp3 - tmp11
    tmp19 = 256.0
    tmp20 = tmp16 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp25 = tmp24 * tmp0
    tmp27 = tmp25 + tmp26
    tmp28 = tmp17 + tmp27
    tl.store(out_ptr0 + (x0), tmp0, None)
    tl.store(in_out_ptr0 + (r3 + (256*x0)), tmp3, None)
    tl.store(in_out_ptr1 + (r3 + (256*x0)), tmp28, None)
    tl.store(out_ptr3 + (x0), tmp23, None)
    tl.store(out_ptr1 + (x0), tmp11, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/b3/cb3qjb4uid2oua44nvmn56hgg22nygnazgnt5dgu6oqhrcyphjio.py
# Topologically Sorted Source Nodes: [conv2d_12, x_29], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   x_29 => add_28, rsqrt_12, var_mean_12
# Graph fragment:
#   %convolution_12 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_25, %primals_50, %primals_51, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_12 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_24, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_28 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_24, 1e-05), kwargs = {})
#   %rsqrt_12 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_28,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_13 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_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.persistent_reduction(
    size_hints=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_13(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.store(out_ptr2 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
    tl.store(out_ptr1 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/qq/cqqyalirw6ktpkb7ck6op5kn5slga5gde6ffhveztg3zuk5kgxda.py
# Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.arange]
# Source node to ATen node mapping:
#   x_31 => iota_26
# Graph fragment:
#   %iota_26 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
triton_poi_fused_arange_14 = async_compile.triton('triton_poi_fused_arange_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_arange_14(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
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/r3/cr3jbyf5ylpcnip7fl3i4e3dqhcl5pfkrdyzumgnsa2b4past5le.py
# Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_31 => add_31, add_32, convert_element_type, convert_element_type_1, mul_26, mul_27
# Graph fragment:
#   %mul_26 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_26, 1), kwargs = {})
#   %add_31 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_26, 0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_31, torch.float32), kwargs = {})
#   %add_32 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
#   %mul_27 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_32, 0.5), kwargs = {})
#   %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_27, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_15 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_15(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_8/inductor_cache/7j/c7jxt7cwj2aeubxvg7i4ecura4xxr7ywbtedfhkmqwrizf2x5a55.py
# Topologically Sorted Source Nodes: [x_30, x_31, x_32], Original ATen: [aten.add, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x_30 => add_30
#   x_31 => _unsafe_index_26
#   x_32 => _unsafe_index_27, _unsafe_index_28
# Graph fragment:
#   %add_30 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_25, %view_25), kwargs = {})
#   %_unsafe_index_26 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_30, [None, None, %unsqueeze_52, %convert_element_type_1]), kwargs = {})
#   %_unsafe_index_27 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_26, [None, None, %sub_11, None]), kwargs = {})
#   %_unsafe_index_28 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_27, [None, None, None, %sub_11]), kwargs = {})
triton_poi_fused__unsafe_index_add_reflection_pad2d_16 = async_compile.triton('triton_poi_fused__unsafe_index_add_reflection_pad2d_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_reflection_pad2d_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_reflection_pad2d_16(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 591872
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 34) % 34
    x0 = xindex % 34
    x4 = (xindex // 1156)
    x2 = (xindex // 1156) % 128
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + (x4), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + (x4), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr5 + (x4), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x2), None, 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)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (tmp8 + (16*tmp4) + (256*x4)), None, eviction_policy='evict_last')
    tmp12 = tmp10 - tmp11
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tmp19 = tmp12 * tmp18
    tmp21 = tmp19 * tmp20
    tmp23 = tmp21 + tmp22
    tmp24 = tmp9 + tmp23
    tl.store(out_ptr0 + (x7), tmp24, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/dq/cdq7haid5a5j3lkr5pvfwpau3a4evwh5wu6wzw4wsmw3e4ska5zp.py
# Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.arange]
# Source node to ATen node mapping:
#   x_34 => iota_30
# Graph fragment:
#   %iota_30 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
triton_poi_fused_arange_17 = async_compile.triton('triton_poi_fused_arange_17', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_arange_17(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lc/clcrsu5s34immb6guobkppggbuvqp4z4ceacadyjt2r2vb5cnfrr.py
# Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_34 => add_35, add_36, convert_element_type_4, convert_element_type_5, mul_30, mul_31
# Graph fragment:
#   %mul_30 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_30, 1), kwargs = {})
#   %add_35 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_30, 0), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_35, torch.float32), kwargs = {})
#   %add_36 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
#   %mul_31 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_36, 0.5), kwargs = {})
#   %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_31, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_18 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_18', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_18(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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_8/inductor_cache/rs/crstzcn5v7cigu6pson46ti5uwlrgj2pre3isfdsds6xfo2g36jv.py
# Topologically Sorted Source Nodes: [conv2d_13, relu_8, x_33, x_34, x_35], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   conv2d_13 => convolution_13
#   relu_8 => relu_8
#   x_33 => relu_9
#   x_34 => _unsafe_index_29
#   x_35 => _unsafe_index_30, _unsafe_index_31
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_28, %primals_54, %primals_55, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%relu_8,), kwargs = {})
#   %_unsafe_index_29 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %unsqueeze_53, %convert_element_type_5]), kwargs = {})
#   %_unsafe_index_30 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_29, [None, None, %sub_6, None]), kwargs = {})
#   %_unsafe_index_31 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_30, [None, None, None, %sub_6]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19', '''
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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1115136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 66) % 66
    x0 = xindex % 66
    x4 = (xindex // 4356)
    x2 = (xindex // 4356) % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-63) + (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], 32, 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 + (32*tmp4) + (1024*x4)), xmask, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tl.store(out_ptr0 + (x7), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kq/ckq7hkv6opreer6lbdx24fqo2yvgdxjmvfszonpciwsoauwqzpih.py
# Topologically Sorted Source Nodes: [conv2d_14, relu_10, x_36, x_37], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   conv2d_14 => convolution_14
#   relu_10 => relu_10
#   x_36 => relu_11
#   x_37 => _unsafe_index_32, _unsafe_index_33
# Graph fragment:
#   %convolution_14 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_31, %primals_56, %primals_57, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_14,), kwargs = {})
#   %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%relu_10,), kwargs = {})
#   %_unsafe_index_32 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_33 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_32, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_20 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_20', '''
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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 663552
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 72
    x1 = (xindex // 72) % 72
    x4 = (xindex // 5184)
    x2 = (xindex // 5184) % 32
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-4) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-4) + x1))))) + (4096*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)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tl.store(out_ptr0 + (x5), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/75/c75bpgwfnqydgwqerxetukjcndwa3ahiwv2cmjwwe5sphuinp6p3.py
# Topologically Sorted Source Nodes: [conv2d_15, relu_12, x_38], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   relu_12 => relu_12
#   x_38 => relu_13
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_33, %primals_58, %primals_59, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_15,), kwargs = {})
#   %relu_13 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%relu_12,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_13, 0), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_12, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', '''
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: '*fp32', 3: '*i1', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 49152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 3
    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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp4 <= tmp6
    tmp8 = tmp5 <= tmp6
    tl.store(out_ptr0 + (x3), tmp5, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
    tl.store(out_ptr2 + (x3), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ka/cka5oxarkfzy467bpcbiwpiacuzwtkeol773jzhj377bep2hegx5.py
# Topologically Sorted Source Nodes: [conv2d_14, relu_10, x_36], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_14 => convolution_14
#   relu_10 => relu_10
#   x_36 => relu_11
# Graph fragment:
#   %convolution_14 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_31, %primals_56, %primals_57, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_14,), kwargs = {})
#   %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%relu_10,), kwargs = {})
#   %le_20 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {})
#   %le_21 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_22 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_22', '''
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: '*i1', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tmp8 = tmp4 <= tmp6
    tl.store(out_ptr0 + (x3), tmp7, None)
    tl.store(out_ptr1 + (x3), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/if/cif7zybwrc56b6c2huvbce7b52l7u2pvnzhnojmneq5z2lg773sv.py
# Topologically Sorted Source Nodes: [conv2d_13, relu_8, x_33], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_13 => convolution_13
#   relu_8 => relu_8
#   x_33 => relu_9
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_28, %primals_54, %primals_55, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%relu_8,), kwargs = {})
#   %le_40 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {})
#   %le_41 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_23 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_23', '''
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: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tmp8 = tmp4 <= tmp6
    tl.store(out_ptr0 + (x3), tmp7, None)
    tl.store(out_ptr1 + (x3), tmp8, 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, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, ), (1, ))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (128, ), (1, ))
    assert_size_stride(primals_12, (128, ), (1, ))
    assert_size_stride(primals_13, (128, ), (1, ))
    assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    assert_size_stride(primals_16, (128, ), (1, ))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, ), (1, ))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, ), (1, ))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128, ), (1, ))
    assert_size_stride(primals_28, (128, ), (1, ))
    assert_size_stride(primals_29, (128, ), (1, ))
    assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (128, ), (1, ))
    assert_size_stride(primals_32, (128, ), (1, ))
    assert_size_stride(primals_33, (128, ), (1, ))
    assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (128, ), (1, ))
    assert_size_stride(primals_36, (128, ), (1, ))
    assert_size_stride(primals_37, (128, ), (1, ))
    assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (128, ), (1, ))
    assert_size_stride(primals_40, (128, ), (1, ))
    assert_size_stride(primals_41, (128, ), (1, ))
    assert_size_stride(primals_42, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_43, (128, ), (1, ))
    assert_size_stride(primals_44, (128, ), (1, ))
    assert_size_stride(primals_45, (128, ), (1, ))
    assert_size_stride(primals_46, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_47, (128, ), (1, ))
    assert_size_stride(primals_48, (128, ), (1, ))
    assert_size_stride(primals_49, (128, ), (1, ))
    assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (128, ), (1, ))
    assert_size_stride(primals_52, (128, ), (1, ))
    assert_size_stride(primals_53, (128, ), (1, ))
    assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_55, (64, ), (1, ))
    assert_size_stride(primals_56, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_57, (32, ), (1, ))
    assert_size_stride(primals_58, (3, 32, 9, 9), (2592, 81, 9, 1))
    assert_size_stride(primals_59, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 62208, grid=grid(62208), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf2 = buf1; del buf1  # reuse
        buf5 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf8 = reinterpret_tensor(buf6, (1, 128, 1, 1), (128, 1, 1, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d, instance_norm], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_convolution_1.run(buf2, buf8, primals_3, buf5, 128, 4096, grid=grid(128), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((128, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_2.run(primals_4, buf3, 128, grid=grid(128), stream=stream0)
        del primals_4
        buf4 = empty_strided_cuda((128, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_2.run(primals_5, buf4, 128, grid=grid(128), stream=stream0)
        del primals_5
        buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 4356, 66, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_3.run(buf2, buf5, buf8, buf3, buf4, buf9, 557568, grid=grid(557568), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf11 = buf10; del buf10  # reuse
        buf14 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.float32)
        buf15 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf17 = reinterpret_tensor(buf15, (1, 256, 1, 1), (256, 1, 1, 1), 0); del buf15  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, instance_norm_1], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_4.run(buf11, buf17, primals_7, buf14, 256, 1024, grid=grid(256), stream=stream0)
        del primals_7
        buf12 = empty_strided_cuda((256, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_1], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_5.run(primals_8, buf12, 256, grid=grid(256), stream=stream0)
        del primals_8
        buf13 = empty_strided_cuda((256, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_1], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_5.run(primals_9, buf13, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf18 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf11, buf14, buf17, buf12, buf13, buf18, 295936, grid=grid(295936), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_10, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf21 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf22 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf20 = buf19; del buf19  # reuse
        buf23 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf26 = reinterpret_tensor(buf24, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf24  # reuse
        buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, instance_norm_2, x_5], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.relu]
        triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7.run(buf20, buf26, primals_12, primals_13, primals_11, buf21, buf22, buf23, buf27, 512, 256, grid=grid(512), stream=stream0)
        del primals_11
        del primals_12
        del primals_13
        buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf27, buf28, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf29 = extern_kernels.convolution(buf28, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf30 = buf29; del buf29  # reuse
        buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf36 = reinterpret_tensor(buf34, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, instance_norm_3], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf30, buf36, primals_15, buf33, 512, 256, grid=grid(512), stream=stream0)
        del primals_15
        buf31 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_3], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_16, buf31, 512, grid=grid(512), stream=stream0)
        del primals_16
        buf32 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_3], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_17, buf32, 512, grid=grid(512), stream=stream0)
        del primals_17
        buf37 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7, x_8], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_11.run(buf30, buf33, buf36, buf31, buf32, buf37, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf40 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf39 = buf38; del buf38  # reuse
        buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf45 = buf27; del buf27  # reuse
        buf44 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_4, x_9, x_10], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12.run(buf39, buf45, primals_20, primals_19, primals_21, buf40, buf41, buf44, 512, 256, grid=grid(512), stream=stream0)
        del primals_19
        del primals_20
        del primals_21
        buf46 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf45, buf46, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf47 = extern_kernels.convolution(buf46, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf48 = buf47; del buf47  # reuse
        buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf52 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf54 = reinterpret_tensor(buf52, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf52  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, instance_norm_5], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf48, buf54, primals_23, buf51, 512, 256, grid=grid(512), stream=stream0)
        del primals_23
        buf49 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_5], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_24, buf49, 512, grid=grid(512), stream=stream0)
        del primals_24
        buf50 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_5], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_25, buf50, 512, grid=grid(512), stream=stream0)
        del primals_25
        buf55 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_12, x_13], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_11.run(buf48, buf51, buf54, buf49, buf50, buf55, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf58 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf57 = buf56; del buf56  # reuse
        buf59 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf63 = buf45; del buf45  # reuse
        buf62 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_6, x_14, x_15], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12.run(buf57, buf63, primals_28, primals_27, primals_29, buf58, buf59, buf62, 512, 256, grid=grid(512), stream=stream0)
        del primals_27
        del primals_28
        del primals_29
        buf64 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf63, buf64, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf65 = extern_kernels.convolution(buf64, primals_30, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf66 = buf65; del buf65  # reuse
        buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf70 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf72 = reinterpret_tensor(buf70, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf70  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, instance_norm_7], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf66, buf72, primals_31, buf69, 512, 256, grid=grid(512), stream=stream0)
        del primals_31
        buf67 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_7], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_32, buf67, 512, grid=grid(512), stream=stream0)
        del primals_32
        buf68 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_7], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_33, buf68, 512, grid=grid(512), stream=stream0)
        del primals_33
        buf73 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_17, x_18], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_11.run(buf66, buf69, buf72, buf67, buf68, buf73, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf74 = extern_kernels.convolution(buf73, primals_34, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf74, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf76 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf75 = buf74; del buf74  # reuse
        buf77 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf81 = buf63; del buf63  # reuse
        buf80 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_8, x_19, x_20], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12.run(buf75, buf81, primals_36, primals_35, primals_37, buf76, buf77, buf80, 512, 256, grid=grid(512), stream=stream0)
        del primals_35
        del primals_36
        del primals_37
        buf82 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf81, buf82, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf83 = extern_kernels.convolution(buf82, primals_38, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf84 = buf83; del buf83  # reuse
        buf87 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf88 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf90 = reinterpret_tensor(buf88, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf88  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, instance_norm_9], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf84, buf90, primals_39, buf87, 512, 256, grid=grid(512), stream=stream0)
        del primals_39
        buf85 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_9], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_40, buf85, 512, grid=grid(512), stream=stream0)
        del primals_40
        buf86 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_9], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_41, buf86, 512, grid=grid(512), stream=stream0)
        del primals_41
        buf91 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_22, x_23], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_11.run(buf84, buf87, buf90, buf85, buf86, buf91, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf92 = extern_kernels.convolution(buf91, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf94 = empty_strided_cuda((512, ), (1, ), torch.float32)
        buf93 = buf92; del buf92  # reuse
        buf95 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf99 = buf81; del buf81  # reuse
        buf98 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_10, x_24, x_25], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12.run(buf93, buf99, primals_44, primals_43, primals_45, buf94, buf95, buf98, 512, 256, grid=grid(512), stream=stream0)
        del primals_43
        del primals_44
        del primals_45
        buf100 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf99, buf100, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf101 = extern_kernels.convolution(buf100, primals_46, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf102 = buf101; del buf101  # reuse
        buf105 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf108 = reinterpret_tensor(buf106, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf106  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, instance_norm_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf102, buf108, primals_47, buf105, 512, 256, grid=grid(512), stream=stream0)
        del primals_47
        buf103 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_11], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_48, buf103, 512, grid=grid(512), stream=stream0)
        del primals_48
        buf104 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_11], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_49, buf104, 512, grid=grid(512), stream=stream0)
        del primals_49
        buf109 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_27, x_28], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_11.run(buf102, buf105, buf108, buf103, buf104, buf109, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf110 = extern_kernels.convolution(buf109, primals_50, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf110, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf111 = buf110; del buf110  # reuse
        buf113 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf114 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_12, x_29], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_13.run(buf111, primals_51, buf113, buf114, buf116, 512, 256, grid=grid(512), stream=stream0)
        del primals_51
        buf112 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_29], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_10.run(primals_52, buf112, 512, grid=grid(512), stream=stream0)
        del primals_52
        buf117 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.arange]
        triton_poi_fused_arange_14.run(buf117, 32, grid=grid(32), stream=stream0)
        buf118 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_31], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_15.run(buf118, 32, grid=grid(32), stream=stream0)
        buf119 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_30, x_31, x_32], Original ATen: [aten.add, aten._unsafe_index, aten.reflection_pad2d]
        triton_poi_fused__unsafe_index_add_reflection_pad2d_16.run(buf118, buf99, buf111, buf113, buf114, buf112, primals_53, buf119, 591872, grid=grid(591872), stream=stream0)
        del buf114
        del buf99
        del primals_53
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf120 = extern_kernels.convolution(buf119, primals_54, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf120, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf121 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.arange]
        triton_poi_fused_arange_17.run(buf121, 64, grid=grid(64), stream=stream0)
        buf122 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_18.run(buf122, 64, grid=grid(64), stream=stream0)
        buf123 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_13, relu_8, x_33, x_34, x_35], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19.run(buf122, buf120, primals_55, buf123, 1115136, grid=grid(1115136), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf124 = extern_kernels.convolution(buf123, primals_56, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf124, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf125 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_14, relu_10, x_36, x_37], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
        triton_poi_fused_convolution_reflection_pad2d_relu_20.run(buf124, primals_57, buf125, 663552, grid=grid(663552), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf126 = extern_kernels.convolution(buf125, primals_58, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf126, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf127 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.float32)
        buf129 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool)
        buf128 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_15, relu_12, x_38], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_21.run(buf126, primals_59, buf127, buf129, buf128, 49152, grid=grid(49152), stream=stream0)
        del buf126
        del primals_59
        buf130 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf131 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_14, relu_10, x_36], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_22.run(buf124, primals_57, buf130, buf131, 524288, grid=grid(524288), stream=stream0)
        del buf124
        del primals_57
        buf132 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf133 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_13, relu_8, x_33], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_23.run(buf120, primals_55, buf132, buf133, 262144, grid=grid(262144), stream=stream0)
        del buf120
        del primals_55
    return (buf127, primals_2, primals_6, primals_10, primals_14, primals_18, primals_22, primals_26, primals_30, primals_34, primals_38, primals_42, primals_46, primals_50, primals_54, primals_56, primals_58, buf0, buf2, buf3, buf4, buf5, buf8, buf9, buf11, buf12, buf13, buf14, buf17, buf18, buf20, buf21, buf22, buf23, buf26, buf28, buf30, buf31, buf32, buf33, buf36, buf37, buf39, buf40, reinterpret_tensor(buf44, (512, ), (1, ), 0), buf46, buf48, buf49, buf50, buf51, buf54, buf55, buf57, buf58, reinterpret_tensor(buf62, (512, ), (1, ), 0), buf64, buf66, buf67, buf68, buf69, buf72, buf73, buf75, buf76, reinterpret_tensor(buf80, (512, ), (1, ), 0), buf82, buf84, buf85, buf86, buf87, buf90, buf91, buf93, buf94, reinterpret_tensor(buf98, (512, ), (1, ), 0), buf100, buf102, buf103, buf104, buf105, buf108, buf109, buf111, buf112, reinterpret_tensor(buf116, (512, ), (1, ), 0), buf117, buf118, buf119, buf121, buf122, buf123, buf125, buf128, buf129, buf130, buf131, buf132, buf133, reinterpret_tensor(buf113, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf95, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf77, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf59, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf41, (1, 512, 1, 1), (512, 1, 1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 3, 9, 9), (243, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, 64, 3, 3), (576, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((3, 32, 9, 9), (2592, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_59 = 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, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59])
    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


class Convlayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
        super().__init__()
        padding = kernel_size // 2
        self.refl = torch.nn.ReflectionPad2d(padding)
        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)

    def forward(self, x):
        x = self.refl(x)
        return self.conv(x)


class Residential(torch.nn.Module):

    def __init__(self, in_channels):
        super().__init__()
        self.conv1 = Convlayer(in_channels=in_channels, out_channels=
            in_channels, kernel_size=3)
        self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
        self.conv2 = Convlayer(in_channels=in_channels, out_channels=
            in_channels, kernel_size=3)
        self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        resident = x
        x = self.relu(self.inst1(self.conv1(x)))
        x = self.inst2(self.conv2(x))
        return resident + x


class UpsampleConvLayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        upsample=None):
        super().__init__()
        self.upsample = upsample
        reflectpad = kernel_size // 2
        self.reflectionpad = torch.nn.ReflectionPad2d(reflectpad)
        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        if self.upsample:
            x = torch.nn.functional.interpolate(x, scale_factor=self.
                upsample, mode='nearest')
        x = self.reflectionpad(x)
        return self.relu(self.conv(x))


class Transformer(torch.nn.Module):
    """
    the min input size is [1, 3, 16, 16] as the kernel size and stride reduce the height and width
    otherwise exception might caused as the input_size != output_size
    """

    def __init__(self):
        super().__init__()
        self.conv1 = Convlayer(in_channels=3, out_channels=32, kernel_size=
            9, stride=1)
        self.inst1 = torch.nn.InstanceNorm2d(num_features=32, affine=True)
        self.conv2 = Convlayer(in_channels=32, out_channels=64, kernel_size
            =3, stride=2)
        self.inst2 = torch.nn.InstanceNorm2d(num_features=64, affine=True)
        self.conv3 = Convlayer(in_channels=64, out_channels=128,
            kernel_size=3, stride=2)
        self.inst3 = torch.nn.InstanceNorm2d(num_features=128, affine=True)
        self.res1 = Residential(128)
        self.res2 = Residential(128)
        self.res3 = Residential(128)
        self.res4 = Residential(128)
        self.res5 = Residential(128)
        self.upsample1 = UpsampleConvLayer(in_channels=128, out_channels=64,
            kernel_size=3, stride=1, upsample=2)
        self.upsample2 = UpsampleConvLayer(in_channels=64, out_channels=32,
            kernel_size=3, stride=1, upsample=2)
        self.upsample3 = UpsampleConvLayer(in_channels=32, out_channels=3,
            kernel_size=9, stride=1)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.relu(self.inst1(self.conv1(x)))
        x = self.relu(self.inst2(self.conv2(x)))
        x = self.relu(self.inst3(self.conv3(x)))
        x = self.res1(x)
        x = self.res2(x)
        x = self.res3(x)
        x = self.res4(x)
        x = self.res5(x)
        x = self.relu(self.upsample1(x))
        x = self.relu(self.upsample2(x))
        x = self.relu(self.upsample3(x))
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


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 libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 62208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 72
    x1 = xindex // 72 % 72
    x2 = xindex // 5184
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 32
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp11, xmask)


@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 32, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 66
    x1 = xindex // 66 % 66
    x2 = xindex // 4356
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_4(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 1024.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_repeat_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 64, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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
    x2 = xindex // 1156
    x3 = 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 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    out_ptr2, out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0 % 128, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_out_ptr0 + (r3 + 256 * x0), None)
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 256, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = 256.0
    tmp19 = tmp17 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp4 - tmp12
    tmp24 = tmp23 * tmp22
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp1
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tl.store(out_ptr0 + x0, tmp0, None)
    tl.store(out_ptr1 + x0, tmp1, None)
    tl.store(in_out_ptr0 + (r3 + 256 * x0), tmp4, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp22, None)
    tl.store(out_ptr3 + (r3 + 256 * x0), tmp28, None)
    tl.store(out_ptr2 + x0, tmp12, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_8(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 % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = 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 * x2),
        None, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_9(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_repeat_10(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 128, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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
    x2 = xindex // 324
    x3 = 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 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 256 * x0), None)
    tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp17 = tl.load(in_out_ptr1 + (r3 + 256 * x0), None)
    tmp26 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tmp9 = tl.full([1], 256, tl.int32)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp8 / tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp18 = tmp3 - tmp11
    tmp19 = 256.0
    tmp20 = tmp16 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp25 = tmp24 * tmp0
    tmp27 = tmp25 + tmp26
    tmp28 = tmp17 + tmp27
    tl.store(out_ptr0 + x0, tmp0, None)
    tl.store(in_out_ptr0 + (r3 + 256 * x0), tmp3, None)
    tl.store(in_out_ptr1 + (r3 + 256 * x0), tmp28, None)
    tl.store(out_ptr3 + x0, tmp23, None)
    tl.store(out_ptr1 + x0, tmp11, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_13(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.store(out_ptr2 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)
    tl.store(out_ptr1 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_arange_14(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
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_15(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_add_reflection_pad2d_16(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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 // 34 % 34
    x0 = xindex % 34
    x4 = xindex // 1156
    x2 = xindex // 1156 % 128
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x1))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0))), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + x4, None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x4, None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr5 + x4, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x2, None, 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), None,
        eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (tmp8 + 16 * tmp4 + 256 * x4), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 - tmp11
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tmp19 = tmp12 * tmp18
    tmp21 = tmp19 * tmp20
    tmp23 = tmp21 + tmp22
    tmp24 = tmp9 + tmp23
    tl.store(out_ptr0 + x7, tmp24, None)


@triton.jit
def triton_poi_fused_arange_17(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_18(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    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_19(in_ptr0
    , in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1115136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 66 % 66
    x0 = xindex % 66
    x4 = xindex // 4356
    x2 = xindex // 4356 % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x1))), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + 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], 32, 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 + 32 * tmp4 + 1024 * x4), xmask,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tl.store(out_ptr0 + x7, tmp14, xmask)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_20(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 % 72
    x1 = xindex // 72 % 72
    x4 = xindex // 5184
    x2 = xindex // 5184 % 32
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * 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)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tl.store(out_ptr0 + x5, tmp5, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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 // 4096 % 3
    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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp4 <= tmp6
    tmp8 = tmp5 <= tmp6
    tl.store(out_ptr0 + x3, tmp5, None)
    tl.store(out_ptr1 + x3, tmp7, None)
    tl.store(out_ptr2 + x3, tmp8, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, 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 // 4096 % 32
    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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tmp8 = tmp4 <= tmp6
    tl.store(out_ptr0 + x3, tmp7, None)
    tl.store(out_ptr1 + x3, tmp8, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, 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 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tmp8 = tmp4 <= tmp6
    tl.store(out_ptr0 + x3, tmp7, None)
    tl.store(out_ptr1 + x3, tmp8, 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, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64,), (1,))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (128,), (1,))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128,), (1,))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128,), (1,))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128,), (1,))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (128,), (1,))
    assert_size_stride(primals_29, (128,), (1,))
    assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (128,), (1,))
    assert_size_stride(primals_32, (128,), (1,))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (128,), (1,))
    assert_size_stride(primals_36, (128,), (1,))
    assert_size_stride(primals_37, (128,), (1,))
    assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (128,), (1,))
    assert_size_stride(primals_40, (128,), (1,))
    assert_size_stride(primals_41, (128,), (1,))
    assert_size_stride(primals_42, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_43, (128,), (1,))
    assert_size_stride(primals_44, (128,), (1,))
    assert_size_stride(primals_45, (128,), (1,))
    assert_size_stride(primals_46, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_47, (128,), (1,))
    assert_size_stride(primals_48, (128,), (1,))
    assert_size_stride(primals_49, (128,), (1,))
    assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (128,), (1,))
    assert_size_stride(primals_52, (128,), (1,))
    assert_size_stride(primals_53, (128,), (1,))
    assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_55, (64,), (1,))
    assert_size_stride(primals_56, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_57, (32,), (1,))
    assert_size_stride(primals_58, (3, 32, 9, 9), (2592, 81, 9, 1))
    assert_size_stride(primals_59, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(62208)](primals_1, buf0, 
            62208, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf2 = buf1
        del buf1
        buf5 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32
            )
        buf6 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch
            .float32)
        buf8 = reinterpret_tensor(buf6, (1, 128, 1, 1), (128, 1, 1, 1), 0)
        del buf6
        triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](buf2
            , buf8, primals_3, buf5, 128, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_4, buf3, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_5, buf4, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 4356, 66, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_3[grid(557568)](buf2, buf5,
            buf8, buf3, buf4, buf9, 557568, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf11 = buf10
        del buf10
        buf14 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
            float32)
        buf15 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf17 = reinterpret_tensor(buf15, (1, 256, 1, 1), (256, 1, 1, 1), 0)
        del buf15
        triton_per_fused__native_batch_norm_legit_convolution_4[grid(256)](
            buf11, buf17, primals_7, buf14, 256, 1024, num_warps=8,
            num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_8, buf12, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_8
        buf13 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_9, buf13, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf18 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(295936)](buf11, buf14,
            buf17, buf12, buf13, buf18, 295936, XBLOCK=512, num_warps=8,
            num_stages=1)
        buf19 = extern_kernels.convolution(buf18, primals_10, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf21 = empty_strided_cuda((512,), (1,), torch.float32)
        buf22 = empty_strided_cuda((512,), (1,), torch.float32)
        buf20 = buf19
        del buf19
        buf23 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf26 = reinterpret_tensor(buf24, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf24
        buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7[
            grid(512)](buf20, buf26, primals_12, primals_13, primals_11,
            buf21, buf22, buf23, buf27, 512, 256, num_warps=2, num_stages=1)
        del primals_11
        del primals_12
        del primals_13
        buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf27, buf28, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf29 = extern_kernels.convolution(buf28, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf30 = buf29
        del buf29
        buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf36 = reinterpret_tensor(buf34, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf34
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf30, buf36, primals_15, buf33, 512, 256, num_warps=2,
            num_stages=1)
        del primals_15
        buf31 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_16, buf31, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_16
        buf32 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_17, buf32, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf37 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf30,
            buf33, buf36, buf31, buf32, buf37, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf38 = extern_kernels.convolution(buf37, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf40 = empty_strided_cuda((512,), (1,), torch.float32)
        buf39 = buf38
        del buf38
        buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf45 = buf27
        del buf27
        buf44 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf39, buf45, primals_20, primals_19, primals_21,
            buf40, buf41, buf44, 512, 256, num_warps=2, num_stages=1)
        del primals_19
        del primals_20
        del primals_21
        buf46 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf45, buf46, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf47 = extern_kernels.convolution(buf46, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf48 = buf47
        del buf47
        buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf52 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf54 = reinterpret_tensor(buf52, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf52
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf48, buf54, primals_23, buf51, 512, 256, num_warps=2,
            num_stages=1)
        del primals_23
        buf49 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_24, buf49, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_24
        buf50 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_25, buf50, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf55 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf48,
            buf51, buf54, buf49, buf50, buf55, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf56 = extern_kernels.convolution(buf55, primals_26, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf58 = empty_strided_cuda((512,), (1,), torch.float32)
        buf57 = buf56
        del buf56
        buf59 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf63 = buf45
        del buf45
        buf62 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf57, buf63, primals_28, primals_27, primals_29,
            buf58, buf59, buf62, 512, 256, num_warps=2, num_stages=1)
        del primals_27
        del primals_28
        del primals_29
        buf64 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf63, buf64, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf65 = extern_kernels.convolution(buf64, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf66 = buf65
        del buf65
        buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf70 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf72 = reinterpret_tensor(buf70, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf70
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf66, buf72, primals_31, buf69, 512, 256, num_warps=2,
            num_stages=1)
        del primals_31
        buf67 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_32, buf67, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_32
        buf68 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_33, buf68, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_33
        buf73 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf66,
            buf69, buf72, buf67, buf68, buf73, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf74 = extern_kernels.convolution(buf73, primals_34, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf74, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf76 = empty_strided_cuda((512,), (1,), torch.float32)
        buf75 = buf74
        del buf74
        buf77 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf81 = buf63
        del buf63
        buf80 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf75, buf81, primals_36, primals_35, primals_37,
            buf76, buf77, buf80, 512, 256, num_warps=2, num_stages=1)
        del primals_35
        del primals_36
        del primals_37
        buf82 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf81, buf82, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf83 = extern_kernels.convolution(buf82, primals_38, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf84 = buf83
        del buf83
        buf87 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf88 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf90 = reinterpret_tensor(buf88, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf88
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf84, buf90, primals_39, buf87, 512, 256, num_warps=2,
            num_stages=1)
        del primals_39
        buf85 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_40, buf85, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_40
        buf86 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_41, buf86, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_41
        buf91 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf84,
            buf87, buf90, buf85, buf86, buf91, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf92 = extern_kernels.convolution(buf91, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf94 = empty_strided_cuda((512,), (1,), torch.float32)
        buf93 = buf92
        del buf92
        buf95 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf99 = buf81
        del buf81
        buf98 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf93, buf99, primals_44, primals_43, primals_45,
            buf94, buf95, buf98, 512, 256, num_warps=2, num_stages=1)
        del primals_43
        del primals_44
        del primals_45
        buf100 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf99, buf100, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf101 = extern_kernels.convolution(buf100, primals_46, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf102 = buf101
        del buf101
        buf105 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf108 = reinterpret_tensor(buf106, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf106
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf102, buf108, primals_47, buf105, 512, 256, num_warps=2,
            num_stages=1)
        del primals_47
        buf103 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_48, buf103, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_48
        buf104 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_49, buf104, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_49
        buf109 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf102,
            buf105, buf108, buf103, buf104, buf109, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf110 = extern_kernels.convolution(buf109, primals_50, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf110, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf111 = buf110
        del buf110
        buf113 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf114 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_13[grid(512)](
            buf111, primals_51, buf113, buf114, buf116, 512, 256, num_warps
            =2, num_stages=1)
        del primals_51
        buf112 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_52, buf112, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_52
        buf117 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused_arange_14[grid(32)](buf117, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf118 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_15[grid(32)](buf118, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf119 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_add_reflection_pad2d_16[grid(591872)](
            buf118, buf99, buf111, buf113, buf114, buf112, primals_53,
            buf119, 591872, XBLOCK=512, num_warps=8, num_stages=1)
        del buf114
        del buf99
        del primals_53
        buf120 = extern_kernels.convolution(buf119, primals_54, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf120, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf121 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused_arange_17[grid(64)](buf121, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf122 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_18[grid(64)](buf122, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf123 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19[
            grid(1115136)](buf122, buf120, primals_55, buf123, 1115136,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf124 = extern_kernels.convolution(buf123, primals_56, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf124, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf125 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_20[grid(663552)](
            buf124, primals_57, buf125, 663552, XBLOCK=512, num_warps=8,
            num_stages=1)
        buf126 = extern_kernels.convolution(buf125, primals_58, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf126, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf127 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        buf129 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        buf128 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_21[grid(49152)](
            buf126, primals_59, buf127, buf129, buf128, 49152, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf126
        del primals_59
        buf130 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf131 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_22[grid(524288)](
            buf124, primals_57, buf130, buf131, 524288, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del buf124
        del primals_57
        buf132 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf133 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_23[grid(262144)](
            buf120, primals_55, buf132, buf133, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del buf120
        del primals_55
    return (buf127, primals_2, primals_6, primals_10, primals_14,
        primals_18, primals_22, primals_26, primals_30, primals_34,
        primals_38, primals_42, primals_46, primals_50, primals_54,
        primals_56, primals_58, buf0, buf2, buf3, buf4, buf5, buf8, buf9,
        buf11, buf12, buf13, buf14, buf17, buf18, buf20, buf21, buf22,
        buf23, buf26, buf28, buf30, buf31, buf32, buf33, buf36, buf37,
        buf39, buf40, reinterpret_tensor(buf44, (512,), (1,), 0), buf46,
        buf48, buf49, buf50, buf51, buf54, buf55, buf57, buf58,
        reinterpret_tensor(buf62, (512,), (1,), 0), buf64, buf66, buf67,
        buf68, buf69, buf72, buf73, buf75, buf76, reinterpret_tensor(buf80,
        (512,), (1,), 0), buf82, buf84, buf85, buf86, buf87, buf90, buf91,
        buf93, buf94, reinterpret_tensor(buf98, (512,), (1,), 0), buf100,
        buf102, buf103, buf104, buf105, buf108, buf109, buf111, buf112,
        reinterpret_tensor(buf116, (512,), (1,), 0), buf117, buf118, buf119,
        buf121, buf122, buf123, buf125, buf128, buf129, buf130, buf131,
        buf132, buf133, reinterpret_tensor(buf113, (1, 512, 1, 1), (512, 1,
        1, 1), 0), reinterpret_tensor(buf95, (1, 512, 1, 1), (512, 1, 1, 1),
        0), reinterpret_tensor(buf77, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf59, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf41, (1, 512, 1, 1), (512, 1, 1, 1), 0))


class Convlayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
        super().__init__()
        padding = kernel_size // 2
        self.refl = torch.nn.ReflectionPad2d(padding)
        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)

    def forward(self, x):
        x = self.refl(x)
        return self.conv(x)


class Residential(torch.nn.Module):

    def __init__(self, in_channels):
        super().__init__()
        self.conv1 = Convlayer(in_channels=in_channels, out_channels=
            in_channels, kernel_size=3)
        self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
        self.conv2 = Convlayer(in_channels=in_channels, out_channels=
            in_channels, kernel_size=3)
        self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        resident = x
        x = self.relu(self.inst1(self.conv1(x)))
        x = self.inst2(self.conv2(x))
        return resident + x


class UpsampleConvLayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        upsample=None):
        super().__init__()
        self.upsample = upsample
        reflectpad = kernel_size // 2
        self.reflectionpad = torch.nn.ReflectionPad2d(reflectpad)
        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        if self.upsample:
            x = torch.nn.functional.interpolate(x, scale_factor=self.
                upsample, mode='nearest')
        x = self.reflectionpad(x)
        return self.relu(self.conv(x))


class TransformerNew(torch.nn.Module):
    """
    the min input size is [1, 3, 16, 16] as the kernel size and stride reduce the height and width
    otherwise exception might caused as the input_size != output_size
    """

    def __init__(self):
        super().__init__()
        self.conv1 = Convlayer(in_channels=3, out_channels=32, kernel_size=
            9, stride=1)
        self.inst1 = torch.nn.InstanceNorm2d(num_features=32, affine=True)
        self.conv2 = Convlayer(in_channels=32, out_channels=64, kernel_size
            =3, stride=2)
        self.inst2 = torch.nn.InstanceNorm2d(num_features=64, affine=True)
        self.conv3 = Convlayer(in_channels=64, out_channels=128,
            kernel_size=3, stride=2)
        self.inst3 = torch.nn.InstanceNorm2d(num_features=128, affine=True)
        self.res1 = Residential(128)
        self.res2 = Residential(128)
        self.res3 = Residential(128)
        self.res4 = Residential(128)
        self.res5 = Residential(128)
        self.upsample1 = UpsampleConvLayer(in_channels=128, out_channels=64,
            kernel_size=3, stride=1, upsample=2)
        self.upsample2 = UpsampleConvLayer(in_channels=64, out_channels=32,
            kernel_size=3, stride=1, upsample=2)
        self.upsample3 = UpsampleConvLayer(in_channels=32, out_channels=3,
            kernel_size=9, stride=1)
        self.relu = torch.nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv1.conv.weight
        primals_3 = self.conv1.conv.bias
        primals_4 = self.inst1.weight
        primals_5 = self.inst1.bias
        primals_6 = self.conv2.conv.weight
        primals_7 = self.conv2.conv.bias
        primals_8 = self.inst2.weight
        primals_9 = self.inst2.bias
        primals_10 = self.conv3.conv.weight
        primals_11 = self.conv3.conv.bias
        primals_12 = self.inst3.weight
        primals_13 = self.inst3.bias
        primals_14 = self.res1.conv1.conv.weight
        primals_15 = self.res1.conv1.conv.bias
        primals_16 = self.res1.inst1.weight
        primals_17 = self.res1.inst1.bias
        primals_18 = self.res1.conv2.conv.weight
        primals_19 = self.res1.conv2.conv.bias
        primals_20 = self.res1.inst2.weight
        primals_21 = self.res1.inst2.bias
        primals_22 = self.res2.conv1.conv.weight
        primals_23 = self.res2.conv1.conv.bias
        primals_24 = self.res2.inst1.weight
        primals_25 = self.res2.inst1.bias
        primals_26 = self.res2.conv2.conv.weight
        primals_27 = self.res2.conv2.conv.bias
        primals_28 = self.res2.inst2.weight
        primals_29 = self.res2.inst2.bias
        primals_30 = self.res3.conv1.conv.weight
        primals_31 = self.res3.conv1.conv.bias
        primals_32 = self.res3.inst1.weight
        primals_33 = self.res3.inst1.bias
        primals_34 = self.res3.conv2.conv.weight
        primals_35 = self.res3.conv2.conv.bias
        primals_36 = self.res3.inst2.weight
        primals_37 = self.res3.inst2.bias
        primals_38 = self.res4.conv1.conv.weight
        primals_39 = self.res4.conv1.conv.bias
        primals_40 = self.res4.inst1.weight
        primals_41 = self.res4.inst1.bias
        primals_42 = self.res4.conv2.conv.weight
        primals_43 = self.res4.conv2.conv.bias
        primals_44 = self.res4.inst2.weight
        primals_45 = self.res4.inst2.bias
        primals_46 = self.res5.conv1.conv.weight
        primals_47 = self.res5.conv1.conv.bias
        primals_48 = self.res5.inst1.weight
        primals_49 = self.res5.inst1.bias
        primals_50 = self.res5.conv2.conv.weight
        primals_51 = self.res5.conv2.conv.bias
        primals_52 = self.res5.inst2.weight
        primals_53 = self.res5.inst2.bias
        primals_54 = self.upsample1.conv.weight
        primals_55 = self.upsample1.conv.bias
        primals_56 = self.upsample2.conv.weight
        primals_57 = self.upsample2.conv.bias
        primals_58 = self.upsample3.conv.weight
        primals_59 = self.upsample3.conv.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,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59])
        return output[0]
