# 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_6/inductor_cache/uo/cuobiyjbapmnhkmwnq4daan4m3vy2y3j6thhvhnvjnoqztnpy23o.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[512, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 512
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (4*x2) + (36*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/j5/cj5nf2owtsdm2zwcezqxpyn63iwddjyadpotkhm2ua52inoqxdcl.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ba/cbayuw2by4w6xwduhs5qdriinmydiep6bpw7fyi37s377up7lrcm.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_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=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/a3/ca3b7ntc5qab6a3zppgzzo333y2m3scz37bvyrzdvlnd555rphzb.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_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=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_3', '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (2048*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cf/ccfnt5hyyesofgusqzmbitwrm26vlhmjkdsrg3a6g22tv3rtydn5.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_3, [4]), kwargs = {})
triton_poi_fused_repeat_4 = async_compile.triton('triton_poi_fused_repeat_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_4', '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_4(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_6/inductor_cache/l4/cl4fsxkro7k3nz2mre7jww2ntimu23g37si3g6awr3yqskhwgk4z.py
# Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   instance_norm => add, rsqrt, var_mean
# Graph fragment:
#   %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_per_fused__native_batch_norm_legit_5 = async_compile.triton('triton_per_fused__native_batch_norm_legit_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.persistent_reduction(
    size_hints=[512, 16],
    reduction_hint=ReductionHint.DEFAULT,
    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_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_5(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((128*r1) + (2048*(x0 // 128)) + (x0 % 128)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + (x0), tmp21, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr1 + (x0), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/p2/cp2goqrssupnefmj75xnb67fojzzsrjyf7hagtb5jvyxtaaq3wvi.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   y => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
triton_poi_fused_relu_6 = async_compile.triton('triton_poi_fused_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=[8192], 
    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_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_relu_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 128
    x2 = (xindex // 2048)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/q2/cq25nzmdyefhavzmubvxikyjxxg7rgpb44xts64tyqzsojcikxl3.py
# Topologically Sorted Source Nodes: [instance_norm_2], Original ATen: [aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   instance_norm_2 => add_4, rsqrt_2, var_mean_2
# Graph fragment:
#   %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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_7 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[512], 
    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__native_batch_norm_legit_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_7(in_ptr0, out_ptr0, out_ptr1, 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 + ((512*(x0 // 128)) + (x0 % 128)), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + (512*(x0 // 128)) + (x0 % 128)), xmask)
    tmp3 = tl.load(in_ptr0 + (256 + (512*(x0 // 128)) + (x0 % 128)), xmask)
    tmp5 = tl.load(in_ptr0 + (384 + (512*(x0 // 128)) + (x0 % 128)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/td/ctdpgwvcimwzpidcyeqig3cxyjcpuy3v5d4ifu2mqyvmflbqbwtu.py
# Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   y_2 => relu_2
# Graph fragment:
#   %relu_2 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
triton_poi_fused_relu_8 = async_compile.triton('triton_poi_fused_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2048], 
    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_relu_8', '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_relu_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 128
    x2 = (xindex // 512)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), 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_6/inductor_cache/y7/cy7lb24nlhupxf7zahkzvl5w3w3yogg24bf3rn2ome6jlqlkf3mh.py
# Topologically Sorted Source Nodes: [add, y_5], Original ATen: [aten.add, aten.relu]
# Source node to ATen node mapping:
#   add => add_10
#   y_5 => relu_4
# Graph fragment:
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_2, %view_9), kwargs = {})
#   %relu_4 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_10,), kwargs = {})
triton_poi_fused_add_relu_9 = async_compile.triton('triton_poi_fused_add_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_9', '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_add_relu_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 128
    x2 = (xindex // 512)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x3), None)
    tmp2 = tl.load(in_ptr2 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = tmp0 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(out_ptr0 + (x3), tmp12, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/k6/ck6bq6leajyympx26kdt3derfxnka3sfkqxapxpbj536hzc42iz5.py
# Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv_transpose2d => convolution_21
# Graph fragment:
#   %convolution_21 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_20, %primals_65, %primals_66, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_convolution_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_10', 'mutated_arg_names': ['in_out_ptr0'], '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_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/du/cdult4edsworsegmywdbscnm64klvu3tyxc5ck5orwwlhewwt7cu.py
# Topologically Sorted Source Nodes: [conv2d_22, y_32], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_22 => convolution_23
#   y_32 => relu_23
# Graph fragment:
#   %convolution_23 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_22, %primals_73, %primals_74, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_23 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_23,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_23, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': '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_11(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (3*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (3*x2) + (48*y1)), tmp6, xmask & ymask)
''', 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, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74 = args
    args.clear()
    assert_size_stride(primals_1, (128, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (128, ), (1, ))
    assert_size_stride(primals_4, (128, ), (1, ))
    assert_size_stride(primals_5, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_6, (128, ), (1, ))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (128, ), (1, ))
    assert_size_stride(primals_11, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_18, (128, ), (1, ))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (128, ), (1, ))
    assert_size_stride(primals_23, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_30, (128, ), (1, ))
    assert_size_stride(primals_31, (128, ), (1, ))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128, ), (1, ))
    assert_size_stride(primals_34, (128, ), (1, ))
    assert_size_stride(primals_35, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_42, (128, ), (1, ))
    assert_size_stride(primals_43, (128, ), (1, ))
    assert_size_stride(primals_44, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_45, (128, ), (1, ))
    assert_size_stride(primals_46, (128, ), (1, ))
    assert_size_stride(primals_47, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_54, (128, ), (1, ))
    assert_size_stride(primals_55, (128, ), (1, ))
    assert_size_stride(primals_56, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_57, (128, ), (1, ))
    assert_size_stride(primals_58, (128, ), (1, ))
    assert_size_stride(primals_59, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_60, (128, ), (1, ))
    assert_size_stride(primals_61, (128, ), (1, ))
    assert_size_stride(primals_62, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_63, (128, ), (1, ))
    assert_size_stride(primals_64, (128, ), (1, ))
    assert_size_stride(primals_65, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_66, (128, ), (1, ))
    assert_size_stride(primals_67, (128, ), (1, ))
    assert_size_stride(primals_68, (128, ), (1, ))
    assert_size_stride(primals_69, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_70, (128, ), (1, ))
    assert_size_stride(primals_71, (128, ), (1, ))
    assert_size_stride(primals_72, (128, ), (1, ))
    assert_size_stride(primals_73, (3, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_74, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((128, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 512, 9, grid=grid(512, 9), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_2, buf1, 16, 16, grid=grid(16, 16), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_5, buf2, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_5
        buf3 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_8, buf3, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_8
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_11, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_11
        buf5 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_14, buf5, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_14
        buf6 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_17, buf6, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_17
        buf7 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_20, buf7, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_20
        buf8 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_23, buf8, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_23
        buf9 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_26, buf9, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_26
        buf10 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_29, buf10, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_29
        buf11 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_32, buf11, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_32
        buf12 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_35, buf12, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_35
        buf13 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_38, buf13, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_38
        buf14 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_41, buf14, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_41
        buf15 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_44, buf15, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_44
        buf16 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_47, buf16, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_47
        buf17 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_50, buf17, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_50
        buf18 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_53, buf18, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_53
        buf19 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_56, buf19, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_56
        buf20 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_59, buf20, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_59
        buf21 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_62, buf21, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_62
        buf22 = empty_strided_cuda((128, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_65, buf22, 16384, 16, grid=grid(16384, 16), stream=stream0)
        del primals_65
        buf23 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_69, buf23, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_69
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf25 = empty_strided_cuda((512, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_3, buf25, 512, grid=grid(512), stream=stream0)
        del primals_3
        buf26 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf27 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf29 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_5.run(buf24, buf26, buf27, buf29, 512, 16, grid=grid(512), stream=stream0)
        buf30 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf24, buf26, buf27, buf25, primals_4, buf30, 8192, grid=grid(8192), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf31 = extern_kernels.convolution(buf30, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf32 = reinterpret_tensor(buf27, (512, ), (1, ), 0); del buf27  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_1], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_6, buf32, 512, grid=grid(512), stream=stream0)
        del primals_6
        buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf36 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_1], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_5.run(buf31, buf33, buf34, buf36, 512, 16, grid=grid(512), stream=stream0)
        buf37 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf31, buf33, buf34, buf32, primals_7, buf37, 8192, grid=grid(8192), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, buf3, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 128, 2, 2), (512, 1, 256, 128))
        buf39 = reinterpret_tensor(buf34, (512, ), (1, ), 0); del buf34  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_2], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_9, buf39, 512, grid=grid(512), stream=stream0)
        del primals_9
        buf40 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_2], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf38, buf40, buf41, 512, grid=grid(512), stream=stream0)
        buf42 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf38, buf40, buf41, buf39, primals_10, buf42, 2048, grid=grid(2048), stream=stream0)
        del primals_10
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, buf4, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 128, 2, 2), (512, 1, 256, 128))
        buf44 = reinterpret_tensor(buf41, (512, ), (1, ), 0); del buf41  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_3], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_12, buf44, 512, grid=grid(512), stream=stream0)
        del primals_12
        buf45 = buf40; del buf40  # reuse
        buf46 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_3], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf43, buf45, buf46, 512, grid=grid(512), stream=stream0)
        buf47 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf43, buf45, buf46, buf44, primals_13, buf47, 2048, grid=grid(2048), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, buf5, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 2, 2), (512, 1, 256, 128))
        buf49 = reinterpret_tensor(buf46, (512, ), (1, ), 0); del buf46  # reuse
        # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_15, buf49, 512, grid=grid(512), stream=stream0)
        del primals_15
        buf50 = buf45; del buf45  # reuse
        buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf48, buf50, buf51, 512, grid=grid(512), stream=stream0)
        buf52 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add, y_5], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf42, buf48, buf50, buf51, buf49, primals_16, buf52, 2048, grid=grid(2048), stream=stream0)
        del primals_16
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf53 = extern_kernels.convolution(buf52, buf6, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 128, 2, 2), (512, 1, 256, 128))
        buf54 = reinterpret_tensor(buf51, (512, ), (1, ), 0); del buf51  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_5], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_18, buf54, 512, grid=grid(512), stream=stream0)
        del primals_18
        buf55 = buf50; del buf50  # reuse
        buf56 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_5], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf53, buf55, buf56, 512, grid=grid(512), stream=stream0)
        buf57 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf53, buf55, buf56, buf54, primals_19, buf57, 2048, grid=grid(2048), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, buf7, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 128, 2, 2), (512, 1, 256, 128))
        buf59 = reinterpret_tensor(buf56, (512, ), (1, ), 0); del buf56  # reuse
        # Topologically Sorted Source Nodes: [y_7], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_21, buf59, 512, grid=grid(512), stream=stream0)
        del primals_21
        buf60 = buf55; del buf55  # reuse
        buf61 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_7], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf58, buf60, buf61, 512, grid=grid(512), stream=stream0)
        buf62 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_1, y_8], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf52, buf58, buf60, buf61, buf59, primals_22, buf62, 2048, grid=grid(2048), stream=stream0)
        del primals_22
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf63 = extern_kernels.convolution(buf62, buf8, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 128, 2, 2), (512, 1, 256, 128))
        buf64 = reinterpret_tensor(buf61, (512, ), (1, ), 0); del buf61  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_7], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_24, buf64, 512, grid=grid(512), stream=stream0)
        del primals_24
        buf65 = buf60; del buf60  # reuse
        buf66 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_7], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf63, buf65, buf66, 512, grid=grid(512), stream=stream0)
        buf67 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf63, buf65, buf66, buf64, primals_25, buf67, 2048, grid=grid(2048), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf68 = extern_kernels.convolution(buf67, buf9, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 128, 2, 2), (512, 1, 256, 128))
        buf69 = reinterpret_tensor(buf66, (512, ), (1, ), 0); del buf66  # reuse
        # Topologically Sorted Source Nodes: [y_10], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_27, buf69, 512, grid=grid(512), stream=stream0)
        del primals_27
        buf70 = buf65; del buf65  # reuse
        buf71 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_10], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf68, buf70, buf71, 512, grid=grid(512), stream=stream0)
        buf72 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_2, y_11], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf62, buf68, buf70, buf71, buf69, primals_28, buf72, 2048, grid=grid(2048), stream=stream0)
        del primals_28
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf73 = extern_kernels.convolution(buf72, buf10, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 128, 2, 2), (512, 1, 256, 128))
        buf74 = reinterpret_tensor(buf71, (512, ), (1, ), 0); del buf71  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_9], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_30, buf74, 512, grid=grid(512), stream=stream0)
        del primals_30
        buf75 = buf70; del buf70  # reuse
        buf76 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_9], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf73, buf75, buf76, 512, grid=grid(512), stream=stream0)
        buf77 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_12], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf73, buf75, buf76, buf74, primals_31, buf77, 2048, grid=grid(2048), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf78 = extern_kernels.convolution(buf77, buf11, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 2, 2), (512, 1, 256, 128))
        buf79 = reinterpret_tensor(buf76, (512, ), (1, ), 0); del buf76  # reuse
        # Topologically Sorted Source Nodes: [y_13], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_33, buf79, 512, grid=grid(512), stream=stream0)
        del primals_33
        buf80 = buf75; del buf75  # reuse
        buf81 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_13], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf78, buf80, buf81, 512, grid=grid(512), stream=stream0)
        buf82 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_3, y_14], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf72, buf78, buf80, buf81, buf79, primals_34, buf82, 2048, grid=grid(2048), stream=stream0)
        del primals_34
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf83 = extern_kernels.convolution(buf82, buf12, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 2, 2), (512, 1, 256, 128))
        buf84 = reinterpret_tensor(buf81, (512, ), (1, ), 0); del buf81  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_11], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_36, buf84, 512, grid=grid(512), stream=stream0)
        del primals_36
        buf85 = buf80; del buf80  # reuse
        buf86 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_11], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf83, buf85, buf86, 512, grid=grid(512), stream=stream0)
        buf87 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_15], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf83, buf85, buf86, buf84, primals_37, buf87, 2048, grid=grid(2048), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf88 = extern_kernels.convolution(buf87, buf13, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf88, (4, 128, 2, 2), (512, 1, 256, 128))
        buf89 = reinterpret_tensor(buf86, (512, ), (1, ), 0); del buf86  # reuse
        # Topologically Sorted Source Nodes: [y_16], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_39, buf89, 512, grid=grid(512), stream=stream0)
        del primals_39
        buf90 = buf85; del buf85  # reuse
        buf91 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_16], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf88, buf90, buf91, 512, grid=grid(512), stream=stream0)
        buf92 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_4, y_17], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf82, buf88, buf90, buf91, buf89, primals_40, buf92, 2048, grid=grid(2048), stream=stream0)
        del primals_40
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf93 = extern_kernels.convolution(buf92, buf14, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf93, (4, 128, 2, 2), (512, 1, 256, 128))
        buf94 = reinterpret_tensor(buf91, (512, ), (1, ), 0); del buf91  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_13], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_42, buf94, 512, grid=grid(512), stream=stream0)
        del primals_42
        buf95 = buf90; del buf90  # reuse
        buf96 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_13], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf93, buf95, buf96, 512, grid=grid(512), stream=stream0)
        buf97 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_18], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf93, buf95, buf96, buf94, primals_43, buf97, 2048, grid=grid(2048), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf98 = extern_kernels.convolution(buf97, buf15, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf98, (4, 128, 2, 2), (512, 1, 256, 128))
        buf99 = reinterpret_tensor(buf96, (512, ), (1, ), 0); del buf96  # reuse
        # Topologically Sorted Source Nodes: [y_19], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_45, buf99, 512, grid=grid(512), stream=stream0)
        del primals_45
        buf100 = buf95; del buf95  # reuse
        buf101 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_19], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf98, buf100, buf101, 512, grid=grid(512), stream=stream0)
        buf102 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_5, y_20], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf92, buf98, buf100, buf101, buf99, primals_46, buf102, 2048, grid=grid(2048), stream=stream0)
        del primals_46
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf103 = extern_kernels.convolution(buf102, buf16, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf103, (4, 128, 2, 2), (512, 1, 256, 128))
        buf104 = reinterpret_tensor(buf101, (512, ), (1, ), 0); del buf101  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_15], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_48, buf104, 512, grid=grid(512), stream=stream0)
        del primals_48
        buf105 = buf100; del buf100  # reuse
        buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_15], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf103, buf105, buf106, 512, grid=grid(512), stream=stream0)
        buf107 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_21], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf103, buf105, buf106, buf104, primals_49, buf107, 2048, grid=grid(2048), stream=stream0)
        del primals_49
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf108 = extern_kernels.convolution(buf107, buf17, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 128, 2, 2), (512, 1, 256, 128))
        buf109 = reinterpret_tensor(buf106, (512, ), (1, ), 0); del buf106  # reuse
        # Topologically Sorted Source Nodes: [y_22], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_51, buf109, 512, grid=grid(512), stream=stream0)
        del primals_51
        buf110 = buf105; del buf105  # reuse
        buf111 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_22], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf108, buf110, buf111, 512, grid=grid(512), stream=stream0)
        buf112 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_6, y_23], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf102, buf108, buf110, buf111, buf109, primals_52, buf112, 2048, grid=grid(2048), stream=stream0)
        del primals_52
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf113 = extern_kernels.convolution(buf112, buf18, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf113, (4, 128, 2, 2), (512, 1, 256, 128))
        buf114 = reinterpret_tensor(buf111, (512, ), (1, ), 0); del buf111  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_17], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_54, buf114, 512, grid=grid(512), stream=stream0)
        del primals_54
        buf115 = buf110; del buf110  # reuse
        buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_17], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf113, buf115, buf116, 512, grid=grid(512), stream=stream0)
        buf117 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_24], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf113, buf115, buf116, buf114, primals_55, buf117, 2048, grid=grid(2048), stream=stream0)
        del primals_55
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf118 = extern_kernels.convolution(buf117, buf19, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf118, (4, 128, 2, 2), (512, 1, 256, 128))
        buf119 = reinterpret_tensor(buf116, (512, ), (1, ), 0); del buf116  # reuse
        # Topologically Sorted Source Nodes: [y_25], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_57, buf119, 512, grid=grid(512), stream=stream0)
        del primals_57
        buf120 = buf115; del buf115  # reuse
        buf121 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_25], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf118, buf120, buf121, 512, grid=grid(512), stream=stream0)
        buf122 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_7, y_26], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf112, buf118, buf120, buf121, buf119, primals_58, buf122, 2048, grid=grid(2048), stream=stream0)
        del primals_58
        # Topologically Sorted Source Nodes: [conv2d_19], Original ATen: [aten.convolution]
        buf123 = extern_kernels.convolution(buf122, buf20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf123, (4, 128, 2, 2), (512, 1, 256, 128))
        buf124 = reinterpret_tensor(buf121, (512, ), (1, ), 0); del buf121  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_19], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_60, buf124, 512, grid=grid(512), stream=stream0)
        del primals_60
        buf125 = buf120; del buf120  # reuse
        buf126 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_19], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf123, buf125, buf126, 512, grid=grid(512), stream=stream0)
        buf127 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_27], Original ATen: [aten.relu]
        triton_poi_fused_relu_8.run(buf123, buf125, buf126, buf124, primals_61, buf127, 2048, grid=grid(2048), stream=stream0)
        del primals_61
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf128 = extern_kernels.convolution(buf127, buf21, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf128, (4, 128, 2, 2), (512, 1, 256, 128))
        buf129 = reinterpret_tensor(buf126, (512, ), (1, ), 0); del buf126  # reuse
        # Topologically Sorted Source Nodes: [y_28], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_63, buf129, 512, grid=grid(512), stream=stream0)
        del primals_63
        buf130 = buf125; del buf125  # reuse
        buf131 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [y_28], Original ATen: [aten._native_batch_norm_legit]
        triton_poi_fused__native_batch_norm_legit_7.run(buf128, buf130, buf131, 512, grid=grid(512), stream=stream0)
        buf132 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128), torch.float32)
        # Topologically Sorted Source Nodes: [add_8, y_29], Original ATen: [aten.add, aten.relu]
        triton_poi_fused_add_relu_9.run(buf122, buf128, buf130, buf131, buf129, primals_64, buf132, 2048, grid=grid(2048), stream=stream0)
        del primals_64
        # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
        buf133 = extern_kernels.convolution(buf132, buf22, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf133, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf134 = buf133; del buf133  # reuse
        # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf134, primals_66, 8192, grid=grid(8192), stream=stream0)
        del primals_66
        buf135 = reinterpret_tensor(buf131, (512, ), (1, ), 0); del buf131  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_21], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_67, buf135, 512, grid=grid(512), stream=stream0)
        del primals_67
        buf136 = buf130; del buf130  # reuse
        buf137 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf139 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_21], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_5.run(buf134, buf136, buf137, buf139, 512, 16, grid=grid(512), stream=stream0)
        buf140 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_30], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf134, buf136, buf137, buf135, primals_68, buf140, 8192, grid=grid(8192), stream=stream0)
        del primals_68
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf141 = extern_kernels.convolution(buf140, buf23, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf141, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf142 = buf141; del buf141  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf142, primals_70, 8192, grid=grid(8192), stream=stream0)
        del primals_70
        buf143 = reinterpret_tensor(buf137, (512, ), (1, ), 0); del buf137  # reuse
        # Topologically Sorted Source Nodes: [instance_norm_22], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_4.run(primals_71, buf143, 512, grid=grid(512), stream=stream0)
        del primals_71
        buf144 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf145 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf147 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm_22], Original ATen: [aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_5.run(buf142, buf144, buf145, buf147, 512, 16, grid=grid(512), stream=stream0)
        buf148 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
        # Topologically Sorted Source Nodes: [y_31], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf142, buf144, buf145, buf143, primals_72, buf148, 8192, grid=grid(8192), stream=stream0)
        del buf145
        del primals_72
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf149 = extern_kernels.convolution(buf148, primals_73, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf149, (4, 3, 4, 4), (48, 1, 12, 3))
        buf150 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        buf151 = empty_strided_cuda((4, 3, 4, 4), (48, 1, 12, 3), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_22, y_32], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_11.run(buf149, primals_74, buf150, buf151, 12, 16, grid=grid(12, 16), stream=stream0)
        del buf149
        del primals_74
    return (buf150, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12, buf13, buf14, buf15, buf16, buf17, buf18, buf19, buf20, buf21, buf22, buf23, primals_73, buf24, buf25, reinterpret_tensor(buf29, (512, ), (1, ), 0), buf30, buf31, buf32, reinterpret_tensor(buf36, (512, ), (1, ), 0), buf37, buf38, buf39, buf42, buf43, buf44, buf47, buf48, buf49, buf52, buf53, buf54, buf57, buf58, buf59, buf62, buf63, buf64, buf67, buf68, buf69, buf72, buf73, buf74, buf77, buf78, buf79, buf82, buf83, buf84, buf87, buf88, buf89, buf92, buf93, buf94, buf97, buf98, buf99, buf102, buf103, buf104, buf107, buf108, buf109, buf112, buf113, buf114, buf117, buf118, buf119, buf122, buf123, buf124, buf127, buf128, buf129, buf132, buf134, buf135, reinterpret_tensor(buf139, (512, ), (1, ), 0), buf140, buf142, buf143, reinterpret_tensor(buf147, (512, ), (1, ), 0), buf148, buf151, reinterpret_tensor(buf144, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf136, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf33, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf26, (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((128, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_59 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_60 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_62 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_63 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_64 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_65 = rand_strided((128, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_66 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_67 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_68 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_69 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_70 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_71 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_72 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_73 = rand_strided((3, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_74 = 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, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73, primals_74])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F


class ResidualBlock(nn.Module):

    def __init__(self, channel_num, dilation=1, group=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=
            dilation, dilation=dilation, groups=group, bias=False)
        self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
        self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=
            dilation, dilation=dilation, groups=group, bias=False)
        self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)

    def forward(self, x):
        y = F.relu(self.norm1(self.conv1(x)))
        y = self.norm2(self.conv2(y))
        return F.relu(x + y)


class HidingRes(nn.Module):

    def __init__(self, in_c=4, out_c=3, only_residual=False, requires_grad=True
        ):
        super(HidingRes, self).__init__()
        self.conv1 = nn.Conv2d(in_c, 128, 3, 1, 1, bias=False)
        self.norm1 = nn.InstanceNorm2d(128, affine=True)
        self.conv2 = nn.Conv2d(128, 128, 3, 1, 1, bias=False)
        self.norm2 = nn.InstanceNorm2d(128, affine=True)
        self.conv3 = nn.Conv2d(128, 128, 3, 2, 1, bias=False)
        self.norm3 = nn.InstanceNorm2d(128, affine=True)
        self.res1 = ResidualBlock(128, dilation=2)
        self.res2 = ResidualBlock(128, dilation=2)
        self.res3 = ResidualBlock(128, dilation=2)
        self.res4 = ResidualBlock(128, dilation=2)
        self.res5 = ResidualBlock(128, dilation=4)
        self.res6 = ResidualBlock(128, dilation=4)
        self.res7 = ResidualBlock(128, dilation=4)
        self.res8 = ResidualBlock(128, dilation=4)
        self.res9 = ResidualBlock(128, dilation=1)
        self.deconv3 = nn.ConvTranspose2d(128, 128, 4, 2, 1)
        self.norm4 = nn.InstanceNorm2d(128, affine=True)
        self.deconv2 = nn.Conv2d(128, 128, 3, 1, 1)
        self.norm5 = nn.InstanceNorm2d(128, affine=True)
        self.deconv1 = nn.Conv2d(128, out_c, 1)
        self.only_residual = only_residual
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, x):
        y = F.relu(self.norm1(self.conv1(x)))
        y = F.relu(self.norm2(self.conv2(y)))
        y = F.relu(self.norm3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.res6(y)
        y = self.res7(y)
        y = self.res8(y)
        y = self.res9(y)
        y = F.relu(self.norm4(self.deconv3(y)))
        y = F.relu(self.norm5(self.deconv2(y)))
        if self.only_residual:
            y = self.deconv1(y)
        else:
            y = F.relu(self.deconv1(y))
        return y


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


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 128 * x2 + 2048 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_repeat_4(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_per_fused__native_batch_norm_legit_5(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (128 * r1 + 2048 * (x0 // 128) + x0 % 128),
        xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_6(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)
    x3 = xindex
    x0 = xindex % 128
    x2 = xindex // 2048
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_7(in_ptr0, out_ptr0, out_ptr1,
    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 + (512 * (x0 // 128) + x0 % 128), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + 512 * (x0 // 128) + x0 % 128), xmask)
    tmp3 = tl.load(in_ptr0 + (256 + 512 * (x0 // 128) + x0 % 128), xmask)
    tmp5 = tl.load(in_ptr0 + (384 + 512 * (x0 // 128) + x0 % 128), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_relu_8(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)
    x3 = xindex
    x0 = xindex % 128
    x2 = xindex // 512
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr4 + x0, 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_poi_fused_add_relu_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 128
    x2 = xindex // 512
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x3, None)
    tmp2 = tl.load(in_ptr2 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr4 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = tmp0 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(out_ptr0 + x3, tmp12, None)


@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    ynumel = 12
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 3
    y1 = yindex // 3
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 3 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 3 * x2 + 48 * y1), tmp6, xmask & ymask)


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, primals_60, primals_61, primals_62,
        primals_63, primals_64, primals_65, primals_66, primals_67,
        primals_68, primals_69, primals_70, primals_71, primals_72,
        primals_73, primals_74) = args
    args.clear()
    assert_size_stride(primals_1, (128, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (128,), (1,))
    assert_size_stride(primals_4, (128,), (1,))
    assert_size_stride(primals_5, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_6, (128,), (1,))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (128,), (1,))
    assert_size_stride(primals_11, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_18, (128,), (1,))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (128,), (1,))
    assert_size_stride(primals_23, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_30, (128,), (1,))
    assert_size_stride(primals_31, (128,), (1,))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (128,), (1,))
    assert_size_stride(primals_35, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_42, (128,), (1,))
    assert_size_stride(primals_43, (128,), (1,))
    assert_size_stride(primals_44, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_45, (128,), (1,))
    assert_size_stride(primals_46, (128,), (1,))
    assert_size_stride(primals_47, (128, 128, 3, 3), (1152, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_54, (128,), (1,))
    assert_size_stride(primals_55, (128,), (1,))
    assert_size_stride(primals_56, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_57, (128,), (1,))
    assert_size_stride(primals_58, (128,), (1,))
    assert_size_stride(primals_59, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_60, (128,), (1,))
    assert_size_stride(primals_61, (128,), (1,))
    assert_size_stride(primals_62, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_63, (128,), (1,))
    assert_size_stride(primals_64, (128,), (1,))
    assert_size_stride(primals_65, (128, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_66, (128,), (1,))
    assert_size_stride(primals_67, (128,), (1,))
    assert_size_stride(primals_68, (128,), (1,))
    assert_size_stride(primals_69, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_70, (128,), (1,))
    assert_size_stride(primals_71, (128,), (1,))
    assert_size_stride(primals_72, (128,), (1,))
    assert_size_stride(primals_73, (3, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_74, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((128, 4, 3, 3), (36, 1, 12, 4), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_0[grid(512, 9)](primals_1, buf0, 512, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_1[grid(16, 16)](primals_2, buf1, 16, 16, XBLOCK=16,
            YBLOCK=16, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_5, buf2, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf3 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_8, buf3, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_11, buf4, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_11
        buf5 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_14, buf5, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf6 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_17, buf6, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_17
        buf7 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_20, buf7, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_20
        buf8 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_23, buf8, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_23
        buf9 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_26, buf9, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_26
        buf10 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_29, buf10, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_29
        buf11 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_32, buf11, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_32
        buf12 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_35, buf12, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_35
        buf13 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_38, buf13, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_38
        buf14 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_41, buf14, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_41
        buf15 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_44, buf15, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_44
        buf16 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_47, buf16, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_47
        buf17 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_50, buf17, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_50
        buf18 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_53, buf18, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_53
        buf19 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_56, buf19, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_56
        buf20 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_59, buf20, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_59
        buf21 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_62, buf21, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_62
        buf22 = empty_strided_cuda((128, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_3[grid(16384, 16)](primals_65, buf22, 16384, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_65
        buf23 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_69, buf23, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_69
        buf24 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf25 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_4[grid(512)](primals_3, buf25, 512, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf26 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf27 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf29 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_5[grid(512)](buf24, buf26,
            buf27, buf29, 512, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf30 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_relu_6[grid(8192)](buf24, buf26, buf27, buf25,
            primals_4, buf30, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf31 = extern_kernels.convolution(buf30, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf32 = reinterpret_tensor(buf27, (512,), (1,), 0)
        del buf27
        triton_poi_fused_repeat_4[grid(512)](primals_6, buf32, 512, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_6
        buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf36 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_5[grid(512)](buf31, buf33,
            buf34, buf36, 512, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf37 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_relu_6[grid(8192)](buf31, buf33, buf34, buf32,
            primals_7, buf37, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf38 = extern_kernels.convolution(buf37, buf3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 128, 2, 2), (512, 1, 256, 128))
        buf39 = reinterpret_tensor(buf34, (512,), (1,), 0)
        del buf34
        triton_poi_fused_repeat_4[grid(512)](primals_9, buf39, 512, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf40 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf38, buf40,
            buf41, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf42 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf38, buf40, buf41, buf39,
            primals_10, buf42, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf43 = extern_kernels.convolution(buf42, buf4, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 128, 2, 2), (512, 1, 256, 128))
        buf44 = reinterpret_tensor(buf41, (512,), (1,), 0)
        del buf41
        triton_poi_fused_repeat_4[grid(512)](primals_12, buf44, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_12
        buf45 = buf40
        del buf40
        buf46 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf43, buf45,
            buf46, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf47 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf43, buf45, buf46, buf44,
            primals_13, buf47, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf48 = extern_kernels.convolution(buf47, buf5, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 2, 2), (512, 1, 256, 128))
        buf49 = reinterpret_tensor(buf46, (512,), (1,), 0)
        del buf46
        triton_poi_fused_repeat_4[grid(512)](primals_15, buf49, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_15
        buf50 = buf45
        del buf45
        buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf48, buf50,
            buf51, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf52 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf42, buf48, buf50, buf51,
            buf49, primals_16, buf52, 2048, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_16
        buf53 = extern_kernels.convolution(buf52, buf6, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 128, 2, 2), (512, 1, 256, 128))
        buf54 = reinterpret_tensor(buf51, (512,), (1,), 0)
        del buf51
        triton_poi_fused_repeat_4[grid(512)](primals_18, buf54, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_18
        buf55 = buf50
        del buf50
        buf56 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf53, buf55,
            buf56, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf57 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf53, buf55, buf56, buf54,
            primals_19, buf57, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf58 = extern_kernels.convolution(buf57, buf7, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 128, 2, 2), (512, 1, 256, 128))
        buf59 = reinterpret_tensor(buf56, (512,), (1,), 0)
        del buf56
        triton_poi_fused_repeat_4[grid(512)](primals_21, buf59, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_21
        buf60 = buf55
        del buf55
        buf61 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf58, buf60,
            buf61, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf62 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf52, buf58, buf60, buf61,
            buf59, primals_22, buf62, 2048, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_22
        buf63 = extern_kernels.convolution(buf62, buf8, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 128, 2, 2), (512, 1, 256, 128))
        buf64 = reinterpret_tensor(buf61, (512,), (1,), 0)
        del buf61
        triton_poi_fused_repeat_4[grid(512)](primals_24, buf64, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_24
        buf65 = buf60
        del buf60
        buf66 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf63, buf65,
            buf66, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf67 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf63, buf65, buf66, buf64,
            primals_25, buf67, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf68 = extern_kernels.convolution(buf67, buf9, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 128, 2, 2), (512, 1, 256, 128))
        buf69 = reinterpret_tensor(buf66, (512,), (1,), 0)
        del buf66
        triton_poi_fused_repeat_4[grid(512)](primals_27, buf69, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_27
        buf70 = buf65
        del buf65
        buf71 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf68, buf70,
            buf71, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf72 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf62, buf68, buf70, buf71,
            buf69, primals_28, buf72, 2048, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_28
        buf73 = extern_kernels.convolution(buf72, buf10, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 128, 2, 2), (512, 1, 256, 128))
        buf74 = reinterpret_tensor(buf71, (512,), (1,), 0)
        del buf71
        triton_poi_fused_repeat_4[grid(512)](primals_30, buf74, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_30
        buf75 = buf70
        del buf70
        buf76 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf73, buf75,
            buf76, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf77 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf73, buf75, buf76, buf74,
            primals_31, buf77, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_31
        buf78 = extern_kernels.convolution(buf77, buf11, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 2, 2), (512, 1, 256, 128))
        buf79 = reinterpret_tensor(buf76, (512,), (1,), 0)
        del buf76
        triton_poi_fused_repeat_4[grid(512)](primals_33, buf79, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_33
        buf80 = buf75
        del buf75
        buf81 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf78, buf80,
            buf81, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf82 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf72, buf78, buf80, buf81,
            buf79, primals_34, buf82, 2048, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_34
        buf83 = extern_kernels.convolution(buf82, buf12, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 2, 2), (512, 1, 256, 128))
        buf84 = reinterpret_tensor(buf81, (512,), (1,), 0)
        del buf81
        triton_poi_fused_repeat_4[grid(512)](primals_36, buf84, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_36
        buf85 = buf80
        del buf80
        buf86 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf83, buf85,
            buf86, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf87 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf83, buf85, buf86, buf84,
            primals_37, buf87, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_37
        buf88 = extern_kernels.convolution(buf87, buf13, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf88, (4, 128, 2, 2), (512, 1, 256, 128))
        buf89 = reinterpret_tensor(buf86, (512,), (1,), 0)
        del buf86
        triton_poi_fused_repeat_4[grid(512)](primals_39, buf89, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_39
        buf90 = buf85
        del buf85
        buf91 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf88, buf90,
            buf91, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf92 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf82, buf88, buf90, buf91,
            buf89, primals_40, buf92, 2048, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_40
        buf93 = extern_kernels.convolution(buf92, buf14, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf93, (4, 128, 2, 2), (512, 1, 256, 128))
        buf94 = reinterpret_tensor(buf91, (512,), (1,), 0)
        del buf91
        triton_poi_fused_repeat_4[grid(512)](primals_42, buf94, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_42
        buf95 = buf90
        del buf90
        buf96 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf93, buf95,
            buf96, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf97 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf93, buf95, buf96, buf94,
            primals_43, buf97, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_43
        buf98 = extern_kernels.convolution(buf97, buf15, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf98, (4, 128, 2, 2), (512, 1, 256, 128))
        buf99 = reinterpret_tensor(buf96, (512,), (1,), 0)
        del buf96
        triton_poi_fused_repeat_4[grid(512)](primals_45, buf99, 512, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_45
        buf100 = buf95
        del buf95
        buf101 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf98,
            buf100, buf101, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf102 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf92, buf98, buf100,
            buf101, buf99, primals_46, buf102, 2048, XBLOCK=256, num_warps=
            4, num_stages=1)
        del primals_46
        buf103 = extern_kernels.convolution(buf102, buf16, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf103, (4, 128, 2, 2), (512, 1, 256, 128))
        buf104 = reinterpret_tensor(buf101, (512,), (1,), 0)
        del buf101
        triton_poi_fused_repeat_4[grid(512)](primals_48, buf104, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_48
        buf105 = buf100
        del buf100
        buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf103,
            buf105, buf106, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf107 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf103, buf105, buf106, buf104,
            primals_49, buf107, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_49
        buf108 = extern_kernels.convolution(buf107, buf17, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 128, 2, 2), (512, 1, 256, 128))
        buf109 = reinterpret_tensor(buf106, (512,), (1,), 0)
        del buf106
        triton_poi_fused_repeat_4[grid(512)](primals_51, buf109, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_51
        buf110 = buf105
        del buf105
        buf111 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf108,
            buf110, buf111, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf112 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf102, buf108, buf110,
            buf111, buf109, primals_52, buf112, 2048, XBLOCK=256, num_warps
            =4, num_stages=1)
        del primals_52
        buf113 = extern_kernels.convolution(buf112, buf18, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf113, (4, 128, 2, 2), (512, 1, 256, 128))
        buf114 = reinterpret_tensor(buf111, (512,), (1,), 0)
        del buf111
        triton_poi_fused_repeat_4[grid(512)](primals_54, buf114, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_54
        buf115 = buf110
        del buf110
        buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf113,
            buf115, buf116, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf117 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf113, buf115, buf116, buf114,
            primals_55, buf117, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_55
        buf118 = extern_kernels.convolution(buf117, buf19, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf118, (4, 128, 2, 2), (512, 1, 256, 128))
        buf119 = reinterpret_tensor(buf116, (512,), (1,), 0)
        del buf116
        triton_poi_fused_repeat_4[grid(512)](primals_57, buf119, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_57
        buf120 = buf115
        del buf115
        buf121 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf118,
            buf120, buf121, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf122 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf112, buf118, buf120,
            buf121, buf119, primals_58, buf122, 2048, XBLOCK=256, num_warps
            =4, num_stages=1)
        del primals_58
        buf123 = extern_kernels.convolution(buf122, buf20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf123, (4, 128, 2, 2), (512, 1, 256, 128))
        buf124 = reinterpret_tensor(buf121, (512,), (1,), 0)
        del buf121
        triton_poi_fused_repeat_4[grid(512)](primals_60, buf124, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_60
        buf125 = buf120
        del buf120
        buf126 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf123,
            buf125, buf126, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf127 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_relu_8[grid(2048)](buf123, buf125, buf126, buf124,
            primals_61, buf127, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_61
        buf128 = extern_kernels.convolution(buf127, buf21, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf128, (4, 128, 2, 2), (512, 1, 256, 128))
        buf129 = reinterpret_tensor(buf126, (512,), (1,), 0)
        del buf126
        triton_poi_fused_repeat_4[grid(512)](primals_63, buf129, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_63
        buf130 = buf125
        del buf125
        buf131 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_poi_fused__native_batch_norm_legit_7[grid(512)](buf128,
            buf130, buf131, 512, XBLOCK=256, num_warps=4, num_stages=1)
        buf132 = empty_strided_cuda((4, 128, 2, 2), (512, 1, 256, 128),
            torch.float32)
        triton_poi_fused_add_relu_9[grid(2048)](buf122, buf128, buf130,
            buf131, buf129, primals_64, buf132, 2048, XBLOCK=256, num_warps
            =4, num_stages=1)
        del primals_64
        buf133 = extern_kernels.convolution(buf132, buf22, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf133, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf134 = buf133
        del buf133
        triton_poi_fused_convolution_10[grid(8192)](buf134, primals_66, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_66
        buf135 = reinterpret_tensor(buf131, (512,), (1,), 0)
        del buf131
        triton_poi_fused_repeat_4[grid(512)](primals_67, buf135, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_67
        buf136 = buf130
        del buf130
        buf137 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf139 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_5[grid(512)](buf134,
            buf136, buf137, buf139, 512, 16, XBLOCK=8, num_warps=2,
            num_stages=1)
        buf140 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_relu_6[grid(8192)](buf134, buf136, buf137, buf135,
            primals_68, buf140, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_68
        buf141 = extern_kernels.convolution(buf140, buf23, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf141, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf142 = buf141
        del buf141
        triton_poi_fused_convolution_10[grid(8192)](buf142, primals_70, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_70
        buf143 = reinterpret_tensor(buf137, (512,), (1,), 0)
        del buf137
        triton_poi_fused_repeat_4[grid(512)](primals_71, buf143, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_71
        buf144 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf145 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf147 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_5[grid(512)](buf142,
            buf144, buf145, buf147, 512, 16, XBLOCK=8, num_warps=2,
            num_stages=1)
        buf148 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
            torch.float32)
        triton_poi_fused_relu_6[grid(8192)](buf142, buf144, buf145, buf143,
            primals_72, buf148, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del buf145
        del primals_72
        buf149 = extern_kernels.convolution(buf148, primals_73, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf149, (4, 3, 4, 4), (48, 1, 12, 3))
        buf150 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32
            )
        buf151 = empty_strided_cuda((4, 3, 4, 4), (48, 1, 12, 3), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_11[grid(12, 16)](
            buf149, primals_74, buf150, buf151, 12, 16, XBLOCK=16, YBLOCK=
            16, num_warps=4, num_stages=1)
        del buf149
        del primals_74
    return (buf150, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
        buf9, buf10, buf11, buf12, buf13, buf14, buf15, buf16, buf17, buf18,
        buf19, buf20, buf21, buf22, buf23, primals_73, buf24, buf25,
        reinterpret_tensor(buf29, (512,), (1,), 0), buf30, buf31, buf32,
        reinterpret_tensor(buf36, (512,), (1,), 0), buf37, buf38, buf39,
        buf42, buf43, buf44, buf47, buf48, buf49, buf52, buf53, buf54,
        buf57, buf58, buf59, buf62, buf63, buf64, buf67, buf68, buf69,
        buf72, buf73, buf74, buf77, buf78, buf79, buf82, buf83, buf84,
        buf87, buf88, buf89, buf92, buf93, buf94, buf97, buf98, buf99,
        buf102, buf103, buf104, buf107, buf108, buf109, buf112, buf113,
        buf114, buf117, buf118, buf119, buf122, buf123, buf124, buf127,
        buf128, buf129, buf132, buf134, buf135, reinterpret_tensor(buf139,
        (512,), (1,), 0), buf140, buf142, buf143, reinterpret_tensor(buf147,
        (512,), (1,), 0), buf148, buf151, reinterpret_tensor(buf144, (1, 
        512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf136, (1, 512,
        1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf33, (1, 512, 1, 1),
        (512, 1, 1, 1), 0), reinterpret_tensor(buf26, (1, 512, 1, 1), (512,
        1, 1, 1), 0))


class ResidualBlock(nn.Module):

    def __init__(self, channel_num, dilation=1, group=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=
            dilation, dilation=dilation, groups=group, bias=False)
        self.norm1 = nn.InstanceNorm2d(channel_num, affine=True)
        self.conv2 = nn.Conv2d(channel_num, channel_num, 3, 1, padding=
            dilation, dilation=dilation, groups=group, bias=False)
        self.norm2 = nn.InstanceNorm2d(channel_num, affine=True)

    def forward(self, x):
        y = F.relu(self.norm1(self.conv1(x)))
        y = self.norm2(self.conv2(y))
        return F.relu(x + y)


class HidingResNew(nn.Module):

    def __init__(self, in_c=4, out_c=3, only_residual=False, requires_grad=True
        ):
        super(HidingResNew, self).__init__()
        self.conv1 = nn.Conv2d(in_c, 128, 3, 1, 1, bias=False)
        self.norm1 = nn.InstanceNorm2d(128, affine=True)
        self.conv2 = nn.Conv2d(128, 128, 3, 1, 1, bias=False)
        self.norm2 = nn.InstanceNorm2d(128, affine=True)
        self.conv3 = nn.Conv2d(128, 128, 3, 2, 1, bias=False)
        self.norm3 = nn.InstanceNorm2d(128, affine=True)
        self.res1 = ResidualBlock(128, dilation=2)
        self.res2 = ResidualBlock(128, dilation=2)
        self.res3 = ResidualBlock(128, dilation=2)
        self.res4 = ResidualBlock(128, dilation=2)
        self.res5 = ResidualBlock(128, dilation=4)
        self.res6 = ResidualBlock(128, dilation=4)
        self.res7 = ResidualBlock(128, dilation=4)
        self.res8 = ResidualBlock(128, dilation=4)
        self.res9 = ResidualBlock(128, dilation=1)
        self.deconv3 = nn.ConvTranspose2d(128, 128, 4, 2, 1)
        self.norm4 = nn.InstanceNorm2d(128, affine=True)
        self.deconv2 = nn.Conv2d(128, 128, 3, 1, 1)
        self.norm5 = nn.InstanceNorm2d(128, affine=True)
        self.deconv1 = nn.Conv2d(128, out_c, 1)
        self.only_residual = only_residual
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.norm1.weight
        primals_4 = self.norm1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.norm2.weight
        primals_7 = self.norm2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.norm3.weight
        primals_10 = self.norm3.bias
        primals_11 = self.res1.conv1.weight
        primals_12 = self.res1.norm1.weight
        primals_13 = self.res1.norm1.bias
        primals_14 = self.res1.conv2.weight
        primals_15 = self.res1.norm2.weight
        primals_16 = self.res1.norm2.bias
        primals_17 = self.res2.conv1.weight
        primals_18 = self.res2.norm1.weight
        primals_19 = self.res2.norm1.bias
        primals_20 = self.res2.conv2.weight
        primals_21 = self.res2.norm2.weight
        primals_22 = self.res2.norm2.bias
        primals_23 = self.res3.conv1.weight
        primals_24 = self.res3.norm1.weight
        primals_25 = self.res3.norm1.bias
        primals_26 = self.res3.conv2.weight
        primals_27 = self.res3.norm2.weight
        primals_28 = self.res3.norm2.bias
        primals_29 = self.res4.conv1.weight
        primals_30 = self.res4.norm1.weight
        primals_31 = self.res4.norm1.bias
        primals_32 = self.res4.conv2.weight
        primals_33 = self.res4.norm2.weight
        primals_34 = self.res4.norm2.bias
        primals_35 = self.res5.conv1.weight
        primals_36 = self.res5.norm1.weight
        primals_37 = self.res5.norm1.bias
        primals_38 = self.res5.conv2.weight
        primals_39 = self.res5.norm2.weight
        primals_40 = self.res5.norm2.bias
        primals_41 = self.res6.conv1.weight
        primals_42 = self.res6.norm1.weight
        primals_43 = self.res6.norm1.bias
        primals_44 = self.res6.conv2.weight
        primals_45 = self.res6.norm2.weight
        primals_46 = self.res6.norm2.bias
        primals_47 = self.res7.conv1.weight
        primals_48 = self.res7.norm1.weight
        primals_49 = self.res7.norm1.bias
        primals_50 = self.res7.conv2.weight
        primals_51 = self.res7.norm2.weight
        primals_52 = self.res7.norm2.bias
        primals_53 = self.res8.conv1.weight
        primals_54 = self.res8.norm1.weight
        primals_55 = self.res8.norm1.bias
        primals_56 = self.res8.conv2.weight
        primals_57 = self.res8.norm2.weight
        primals_58 = self.res8.norm2.bias
        primals_59 = self.res9.conv1.weight
        primals_60 = self.res9.norm1.weight
        primals_61 = self.res9.norm1.bias
        primals_62 = self.res9.conv2.weight
        primals_63 = self.res9.norm2.weight
        primals_64 = self.res9.norm2.bias
        primals_65 = self.deconv3.weight
        primals_66 = self.deconv3.bias
        primals_67 = self.norm4.weight
        primals_68 = self.norm4.bias
        primals_69 = self.deconv2.weight
        primals_70 = self.deconv2.bias
        primals_71 = self.norm5.weight
        primals_72 = self.norm5.bias
        primals_73 = self.deconv1.weight
        primals_74 = self.deconv1.bias
        primals_2 = 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,
            primals_60, primals_61, primals_62, primals_63, primals_64,
            primals_65, primals_66, primals_67, primals_68, primals_69,
            primals_70, primals_71, primals_72, primals_73, primals_74])
        return output[0]
