# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/2s/c2s6tatu5w2k7wyrf5wiu4vkmwmy3kfome6gzh546wo6g6rygwhz.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fh/cfhjbprraanxno4skuumwklf3qg74s3y3xtn2udh6tlrkzlx4g7b.py
# Topologically Sorted Source Nodes: [conv2d_1, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   relu => relu
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/eq/ceqrejrlz6hoktu6ql2t3qfkwds72qhu4cilivsev6wjgytsrb4n.py
# Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_4, %primals_12, %primals_13, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(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)
    x3 = xindex
    x1 = (xindex // 16) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/sv/csvcwcefyko454tjebbc2ssqpmasxifbvuqonrhummoqtacpzfoe.py
# Topologically Sorted Source Nodes: [conv2d_6, relu_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   relu_2 => relu_2
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_5, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_3(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)
    x3 = xindex
    x1 = (xindex // 16) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/2k/c2kol25ubn55rikne3xonkdp4xrrmxdfspn65vl26yt2bzj6bnzf.py
# Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_10
# Graph fragment:
#   %convolution_10 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_9, %primals_22, %primals_23, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/cn/ccndf4h57wzmfrfagsb6k6p5n3rfebiqjhlkdvjn5mbhv63qi66q.py
# Topologically Sorted Source Nodes: [conv2d_11, relu_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   relu_4 => relu_4
# Graph fragment:
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_10, %primals_24, %primals_25, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
triton_poi_fused_convolution_relu_5 = async_compile.triton('triton_poi_fused_convolution_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/i3/ci3zkqmbovwjpbffm2akxcvvh65mjepokdhuc4qxthdpoc7gzy3w.py
# Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
# Graph fragment:
#   %convolution_15 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_14, %primals_32, %primals_33, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_6 = async_compile.triton('triton_poi_fused_convolution_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_6(in_out_ptr0, in_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)
    x2 = xindex
    x0 = xindex % 512
    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_1/inductor_cache/se/csejaotbyeq54fbb673ir7yb2qmye6afiljomhs6dc5p6wizydvv.py
# Topologically Sorted Source Nodes: [conv2d_16, relu_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_16 => convolution_16
#   relu_6 => relu_6
# Graph fragment:
#   %convolution_16 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_15, %primals_34, %primals_35, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_16,), kwargs = {})
triton_poi_fused_convolution_relu_7 = async_compile.triton('triton_poi_fused_convolution_relu_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/by/cbys4beaiiwr2dpq5rq25thj72sshl3ndhkdyvtjkr5lkovfhefq.py
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   interpolate => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
triton_poi_fused__to_copy_8 = async_compile.triton('triton_poi_fused__to_copy_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=[2], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_8(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.full([1], 0, tl.int64)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/sm/csmuvlxjzb7v3j25twzjyrzfzrkz2ikt6wzd2vfya4r4kbsym5a7.py
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   interpolate => clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul, sub
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (2,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 0.0), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
triton_poi_fused__to_copy_arange_clamp_mul_sub_9 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_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=[2], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_9(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fo/cfojpldtveoa6274yjf4i5bynfs2ttyy3cmmvy2mocn7oofgayic.py
# Topologically Sorted Source Nodes: [maskd3, interpolate], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   interpolate => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_3, add_4, mul_2, mul_3, mul_4, sub_1, sub_2, sub_4
#   maskd3 => convolution_19
# Graph fragment:
#   %convolution_19 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_40, %primals_41, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_19, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_19, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_19, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_19, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %clamp_max_2), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %clamp_max_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %add_2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_3), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_4), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_10 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_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: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = (xindex // 2) % 2
    x0 = xindex % 2
    x5 = (xindex // 4)
    x2 = (xindex // 4) % 512
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (x5), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 1, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tmp11 - tmp11
    tmp18 = tmp16 * tmp17
    tmp19 = tmp11 + tmp18
    tmp21 = tmp20 + tmp1
    tmp22 = tmp20 < 0
    tmp23 = tl.where(tmp22, tmp21, tmp20)
    tmp24 = tmp19 - tmp19
    tmp26 = tmp24 * tmp25
    tmp27 = tmp19 + tmp26
    tl.store(in_out_ptr0 + (x6), tmp27, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qd/cqdaov2f7mujbbfllp427mcbegppcxkkmkjik63zu6p5tkagq3h6.py
# Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   interpolate_1 => convert_element_type_5
# Graph fragment:
#   %convert_element_type_5 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_2, torch.int64), kwargs = {})
triton_poi_fused__to_copy_11 = async_compile.triton('triton_poi_fused__to_copy_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_11(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/t7/ct7ywxkuqgnrcwinjj3met7mgznwlbfp2rndurv7vbgxo7h2vtgp.py
# Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   interpolate_1 => add_6, clamp_max_4
# Graph fragment:
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_5, 1), kwargs = {})
#   %clamp_max_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_6, 1), kwargs = {})
triton_poi_fused_add_clamp_12 = async_compile.triton('triton_poi_fused_add_clamp_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_12(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.minimum(tmp8, tmp7)
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/i3/ci3ppo4chw5tc5jjqya7ggxfnysin6ygq5nblh6sd24twg6cc3uj.py
# Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   interpolate_1 => clamp_max_6, clamp_min_4, clamp_min_6, convert_element_type_4, iota_2, mul_5, sub_5
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_2, torch.float32), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_4, 0.3333333333333333), kwargs = {})
#   %clamp_min_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_5, 0.0), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_4, %convert_element_type_7), kwargs = {})
#   %clamp_min_6 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_5, 0.0), kwargs = {})
#   %clamp_max_6 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_6, 1.0), kwargs = {})
triton_poi_fused__to_copy_arange_clamp_mul_sub_13 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_13(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/aq/caq7xctf3mfxwaeyh3mczlkq6rslzx7wgwqwbm6bzsr7wpoaya2r.py
# Topologically Sorted Source Nodes: [conv2d_23, masku2, interpolate_1], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.sub, aten.mul]
# Source node to ATen node mapping:
#   conv2d_23 => convolution_23
#   interpolate_1 => _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, add_10, add_8, add_9, mul_7, mul_8, mul_9, sub_6, sub_7, sub_9
#   masku2 => add_5
# Graph fragment:
#   %convolution_23 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_9, %primals_48, %primals_49, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_5 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_23, %convolution_14), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %convert_element_type_5, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %convert_element_type_5, %clamp_max_5]), kwargs = {})
#   %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %clamp_max_4, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %clamp_max_4, %clamp_max_5]), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_5, %_unsafe_index_4), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_6), kwargs = {})
#   %add_8 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_4, %mul_7), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_7, %_unsafe_index_6), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_7, %clamp_max_6), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_6, %mul_8), kwargs = {})
#   %sub_9 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_9, %add_8), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_9, %clamp_max_7), kwargs = {})
#   %add_10 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %mul_9), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_14 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_14', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_14(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x6 = (xindex // 16)
    x2 = (xindex // 16) % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 2, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.load(in_ptr4 + (tmp8 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp13 = tmp11 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp8 + (2*tmp17) + (4*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = tl.load(in_ptr4 + (tmp8 + (2*tmp17) + (4*x6)), None, eviction_policy='evict_last')
    tmp21 = tmp19 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp25 + (2*tmp17) + (4*x6)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr4 + (tmp25 + (2*tmp17) + (4*x6)), None, eviction_policy='evict_last')
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 - tmp21
    tmp32 = tmp30 * tmp31
    tmp33 = tmp21 + tmp32
    tmp34 = tl.load(in_ptr2 + (tmp25 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp35 = tmp34 + tmp10
    tmp36 = tl.load(in_ptr4 + (tmp25 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 - tmp13
    tmp39 = tmp38 * tmp31
    tmp40 = tmp13 + tmp39
    tmp41 = tmp40 - tmp33
    tmp43 = tmp41 * tmp42
    tmp44 = tmp33 + tmp43
    tl.store(in_out_ptr1 + (x4), tmp44, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/r2/cr2dupc2gcikps7nron43qumrfogupug6zdbfy3phtcjrtpxwfw3.py
# Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   interpolate_2 => convert_element_type_9
# Graph fragment:
#   %convert_element_type_9 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_4, torch.int64), kwargs = {})
triton_poi_fused__to_copy_15 = async_compile.triton('triton_poi_fused__to_copy_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_15(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/a7/ca7zrclqowuf2hepio4y3dhfrjto4ksqha4mawazj427vxoc5hlz.py
# Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   interpolate_2 => add_12, clamp_max_8
# Graph fragment:
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_9, 1), kwargs = {})
#   %clamp_max_8 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_12, 3), kwargs = {})
triton_poi_fused_add_clamp_16 = async_compile.triton('triton_poi_fused_add_clamp_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_16(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 3, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zt/cztlvq2s4iomi77y3r26kpilx3gauqxojqmyowp6tt5eko3xqs5m.py
# Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   interpolate_2 => clamp_max_10, clamp_min_10, clamp_min_8, convert_element_type_8, iota_4, mul_10, sub_10
# Graph fragment:
#   %iota_4 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_4, torch.float32), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_8, 0.42857142857142855), kwargs = {})
#   %clamp_min_8 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_10, 0.0), kwargs = {})
#   %sub_10 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_8, %convert_element_type_11), kwargs = {})
#   %clamp_min_10 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_10, 0.0), kwargs = {})
#   %clamp_max_10 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_10, 1.0), kwargs = {})
triton_poi_fused__to_copy_arange_clamp_mul_sub_17 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_17(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/dx/cdx537eqq2vqatonhtvpzy2cyimfja5ok3m3tsjgbctiruggqp57.py
# Topologically Sorted Source Nodes: [conv2d_27, masku1, interpolate_2], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.sub, aten.mul]
# Source node to ATen node mapping:
#   conv2d_27 => convolution_27
#   interpolate_2 => _unsafe_index_10, _unsafe_index_11, _unsafe_index_8, _unsafe_index_9, add_14, add_15, add_16, mul_12, mul_13, mul_14, sub_11, sub_12, sub_14
#   masku1 => add_11
# Graph fragment:
#   %convolution_27 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_11, %primals_56, %primals_57, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_11 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_27, %convolution_9), kwargs = {})
#   %_unsafe_index_8 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_11, [None, None, %convert_element_type_9, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_9 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_11, [None, None, %convert_element_type_9, %clamp_max_9]), kwargs = {})
#   %_unsafe_index_10 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_11, [None, None, %clamp_max_8, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_11, [None, None, %clamp_max_8, %clamp_max_9]), kwargs = {})
#   %sub_11 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_9, %_unsafe_index_8), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_11, %clamp_max_10), kwargs = {})
#   %add_14 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_8, %mul_12), kwargs = {})
#   %sub_12 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_11, %_unsafe_index_10), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_12, %clamp_max_10), kwargs = {})
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_10, %mul_13), kwargs = {})
#   %sub_14 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_15, %add_14), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_14, %clamp_max_11), kwargs = {})
#   %add_16 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_14, %mul_14), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_18 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_18', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_18(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x6 = (xindex // 64)
    x2 = (xindex // 64) % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.load(in_ptr4 + (tmp8 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp13 = tmp11 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp8 + (4*tmp17) + (16*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = tl.load(in_ptr4 + (tmp8 + (4*tmp17) + (16*x6)), None, eviction_policy='evict_last')
    tmp21 = tmp19 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp25 + (4*tmp17) + (16*x6)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr4 + (tmp25 + (4*tmp17) + (16*x6)), None, eviction_policy='evict_last')
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 - tmp21
    tmp32 = tmp30 * tmp31
    tmp33 = tmp21 + tmp32
    tmp34 = tl.load(in_ptr2 + (tmp25 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp35 = tmp34 + tmp10
    tmp36 = tl.load(in_ptr4 + (tmp25 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 - tmp13
    tmp39 = tmp38 * tmp31
    tmp40 = tmp13 + tmp39
    tmp41 = tmp40 - tmp33
    tmp43 = tmp41 * tmp42
    tmp44 = tmp33 + tmp43
    tl.store(in_out_ptr1 + (x4), tmp44, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/b7/cb7kzrsc76xocs733g4vnswm2lkf2lwxr4fppfcwbuhxuv2jbzty.py
# Topologically Sorted Source Nodes: [conv2d_31, masku0], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv2d_31 => convolution_31
#   masku0 => add_17
# Graph fragment:
#   %convolution_31 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_13, %primals_64, %primals_65, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_17 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_31, %convolution_4), kwargs = {})
triton_poi_fused_add_convolution_19 = async_compile.triton('triton_poi_fused_add_convolution_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_19', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_19(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ow/cow5hpqjlh6ifzomvrwg5lpbruem6heswptfhacsgmcx23qbpvkt.py
# Topologically Sorted Source Nodes: [mask], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   mask => convolution_32
# Graph fragment:
#   %convolution_32 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_17, %primals_66, %primals_67, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_20 = async_compile.triton('triton_poi_fused_convolution_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_20', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_20(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6656
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 64) % 26
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (64, ), (1, ))
    assert_size_stride(primals_12, (128, 64, 3, 3), (576, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (256, ), (1, ))
    assert_size_stride(primals_24, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_25, (256, ), (1, ))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256, ), (1, ))
    assert_size_stride(primals_28, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_29, (256, ), (1, ))
    assert_size_stride(primals_30, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_31, (256, ), (1, ))
    assert_size_stride(primals_32, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_33, (512, ), (1, ))
    assert_size_stride(primals_34, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (512, ), (1, ))
    assert_size_stride(primals_36, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_37, (512, ), (1, ))
    assert_size_stride(primals_38, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_39, (512, ), (1, ))
    assert_size_stride(primals_40, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (512, ), (1, ))
    assert_size_stride(primals_42, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_43, (256, ), (1, ))
    assert_size_stride(primals_44, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (256, ), (1, ))
    assert_size_stride(primals_46, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (256, ), (1, ))
    assert_size_stride(primals_48, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_49, (256, ), (1, ))
    assert_size_stride(primals_50, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_51, (128, ), (1, ))
    assert_size_stride(primals_52, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_53, (128, ), (1, ))
    assert_size_stride(primals_54, (128, 128, 3, 3), (1152, 9, 3, 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, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_59, (64, ), (1, ))
    assert_size_stride(primals_60, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (64, ), (1, ))
    assert_size_stride(primals_62, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_63, (64, ), (1, ))
    assert_size_stride(primals_64, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_65, (64, ), (1, ))
    assert_size_stride(primals_66, (26, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_67, (26, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, relu], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 16384, grid=grid(16384), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf5, primals_7, 16384, grid=grid(16384), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, relu_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf7, primals_9, 16384, grid=grid(16384), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [maskd0], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [maskd0], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf9, primals_11, 16384, grid=grid(16384), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf11, primals_13, 8192, grid=grid(8192), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, relu_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf13, primals_15, 8192, grid=grid(8192), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf15, primals_17, 8192, grid=grid(8192), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, relu_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf17, primals_19, 8192, grid=grid(8192), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [maskd1], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [maskd1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf19, primals_21, 8192, grid=grid(8192), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_22, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf21, primals_23, 4096, grid=grid(4096), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, relu_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf23, primals_25, 4096, grid=grid(4096), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf25, primals_27, 4096, grid=grid(4096), stream=stream0)
        del primals_27
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, relu_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf27, primals_29, 4096, grid=grid(4096), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [maskd2], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf29 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [maskd2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf29, primals_31, 4096, grid=grid(4096), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 512, 1, 1), (512, 1, 1, 1))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf31, primals_33, 2048, grid=grid(2048), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 1, 1), (512, 1, 1, 1))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16, relu_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_7.run(buf33, primals_35, 2048, grid=grid(2048), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 512, 1, 1), (512, 1, 1, 1))
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf35, primals_37, 2048, grid=grid(2048), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 1, 1), (512, 1, 1, 1))
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_18, relu_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_7.run(buf37, primals_39, 2048, grid=grid(2048), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [maskd3], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 1, 1), (512, 1, 1, 1))
        buf39 = empty_strided_cuda((2, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_8.run(buf39, 2, grid=grid(2), stream=stream0)
        buf40 = empty_strided_cuda((2, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused__to_copy_8.run(buf40, 2, grid=grid(2), stream=stream0)
        buf41 = empty_strided_cuda((2, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_8.run(buf41, 2, grid=grid(2), stream=stream0)
        buf42 = empty_strided_cuda((2, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused__to_copy_8.run(buf42, 2, grid=grid(2), stream=stream0)
        buf43 = empty_strided_cuda((2, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_9.run(buf43, 2, grid=grid(2), stream=stream0)
        buf45 = empty_strided_cuda((2, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_9.run(buf45, 2, grid=grid(2), stream=stream0)
        buf44 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.float32)
        buf46 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [maskd3, interpolate], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_10.run(buf46, buf39, buf41, buf38, primals_41, buf42, buf43, buf40, buf45, 8192, grid=grid(8192), stream=stream0)
        del buf38
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf47 = extern_kernels.convolution(buf46, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf48 = buf47; del buf47  # reuse
        # Topologically Sorted Source Nodes: [conv2d_20, relu_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf48, primals_43, 4096, grid=grid(4096), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf49 = extern_kernels.convolution(buf48, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf50 = buf49; del buf49  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf50, primals_45, 4096, grid=grid(4096), stream=stream0)
        del primals_45
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf52 = buf51; del buf51  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22, relu_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf52, primals_47, 4096, grid=grid(4096), stream=stream0)
        del primals_47
        # Topologically Sorted Source Nodes: [conv2d_23], Original ATen: [aten.convolution]
        buf53 = extern_kernels.convolution(buf52, primals_48, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf54 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_11.run(buf54, 4, grid=grid(4), stream=stream0)
        buf55 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_12.run(buf55, 4, grid=grid(4), stream=stream0)
        buf56 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_11.run(buf56, 4, grid=grid(4), stream=stream0)
        buf57 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_12.run(buf57, 4, grid=grid(4), stream=stream0)
        buf60 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_13.run(buf60, 4, grid=grid(4), stream=stream0)
        buf62 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate_1], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_13.run(buf62, 4, grid=grid(4), stream=stream0)
        buf59 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        buf63 = buf59; del buf59  # reuse
        buf64 = buf63; del buf63  # reuse
        # Topologically Sorted Source Nodes: [conv2d_23, masku2, interpolate_1], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.sub, aten.mul]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_14.run(buf64, buf55, buf56, buf53, primals_49, buf29, buf54, buf57, buf60, buf62, 16384, grid=grid(16384), stream=stream0)
        del buf53
        del primals_49
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        buf65 = extern_kernels.convolution(buf64, primals_50, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf66 = buf65; del buf65  # reuse
        # Topologically Sorted Source Nodes: [conv2d_24, relu_10], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf66, primals_51, 8192, grid=grid(8192), stream=stream0)
        del primals_51
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        buf67 = extern_kernels.convolution(buf66, primals_52, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf68 = buf67; del buf67  # reuse
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf68, primals_53, 8192, grid=grid(8192), stream=stream0)
        del primals_53
        # Topologically Sorted Source Nodes: [conv2d_26], Original ATen: [aten.convolution]
        buf69 = extern_kernels.convolution(buf68, primals_54, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf70 = buf69; del buf69  # reuse
        # Topologically Sorted Source Nodes: [conv2d_26, relu_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf70, primals_55, 8192, grid=grid(8192), stream=stream0)
        del primals_55
        # Topologically Sorted Source Nodes: [conv2d_27], Original ATen: [aten.convolution]
        buf71 = extern_kernels.convolution(buf70, primals_56, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf71, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf72 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_15.run(buf72, 8, grid=grid(8), stream=stream0)
        buf73 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_16.run(buf73, 8, grid=grid(8), stream=stream0)
        buf74 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_15.run(buf74, 8, grid=grid(8), stream=stream0)
        buf75 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_16.run(buf75, 8, grid=grid(8), stream=stream0)
        buf78 = empty_strided_cuda((8, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_17.run(buf78, 8, grid=grid(8), stream=stream0)
        buf80 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [interpolate_2], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_17.run(buf80, 8, grid=grid(8), stream=stream0)
        buf77 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        buf81 = buf77; del buf77  # reuse
        buf82 = buf81; del buf81  # reuse
        # Topologically Sorted Source Nodes: [conv2d_27, masku1, interpolate_2], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.sub, aten.mul]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_18.run(buf82, buf73, buf74, buf71, primals_57, buf19, buf72, buf75, buf78, buf80, 32768, grid=grid(32768), stream=stream0)
        del buf71
        del primals_57
        # Topologically Sorted Source Nodes: [conv2d_28], Original ATen: [aten.convolution]
        buf83 = extern_kernels.convolution(buf82, primals_58, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf84 = buf83; del buf83  # reuse
        # Topologically Sorted Source Nodes: [conv2d_28, relu_12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf84, primals_59, 16384, grid=grid(16384), stream=stream0)
        del primals_59
        # Topologically Sorted Source Nodes: [conv2d_29], Original ATen: [aten.convolution]
        buf85 = extern_kernels.convolution(buf84, primals_60, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf85, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf86 = buf85; del buf85  # reuse
        # Topologically Sorted Source Nodes: [conv2d_29], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf86, primals_61, 16384, grid=grid(16384), stream=stream0)
        del primals_61
        # Topologically Sorted Source Nodes: [conv2d_30], Original ATen: [aten.convolution]
        buf87 = extern_kernels.convolution(buf86, primals_62, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf88 = buf87; del buf87  # reuse
        # Topologically Sorted Source Nodes: [conv2d_30, relu_13], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf88, primals_63, 16384, grid=grid(16384), stream=stream0)
        del primals_63
        # Topologically Sorted Source Nodes: [conv2d_31], Original ATen: [aten.convolution]
        buf89 = extern_kernels.convolution(buf88, primals_64, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf89, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf90 = buf89; del buf89  # reuse
        # Topologically Sorted Source Nodes: [conv2d_31, masku0], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_19.run(buf90, primals_65, buf9, 16384, grid=grid(16384), stream=stream0)
        del primals_65
        # Topologically Sorted Source Nodes: [mask], Original ATen: [aten.convolution]
        buf91 = extern_kernels.convolution(buf90, primals_66, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf91, (4, 26, 8, 8), (1664, 64, 8, 1))
        buf92 = buf91; del buf91  # reuse
        # Topologically Sorted Source Nodes: [mask], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_20.run(buf92, primals_67, 6656, grid=grid(6656), stream=stream0)
        del primals_67
    return (buf92, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, primals_32, primals_34, primals_36, primals_38, primals_40, primals_42, primals_44, primals_46, primals_48, primals_50, primals_52, primals_54, primals_56, primals_58, primals_60, primals_62, primals_64, primals_66, buf1, buf3, buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf29, buf31, buf33, buf35, buf37, buf39, buf40, buf41, buf42, buf43, buf45, buf46, buf48, buf50, buf52, buf54, buf55, buf56, buf57, buf60, buf62, buf64, buf66, buf68, buf70, buf72, buf73, buf74, buf75, buf78, buf80, buf82, buf84, buf86, buf88, buf90, )


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((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 8, 8), (256, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 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((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((128, 256, 3, 3), (2304, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 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((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_59 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_60 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_62 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_63 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_64 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_65 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_66 = rand_strided((26, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_67 = rand_strided((26, ), (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])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


class ResBlock(nn.Module):

    def __init__(self, in_channel, out_channel, ker_size, stri, pad):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channel, out_channel, 3, 1, 1)
        self.conv2 = nn.Conv2d(out_channel, out_channel, 3, 1, 1)

    def forward(self, x):
        return self.conv2(F.relu(self.conv1(x)))


class ada_mask(nn.Module):

    def __init__(self, input_channel):
        super(ada_mask, self).__init__()
        self.mask_head = nn.Conv2d(input_channel, 64, 3, 1, 1)
        self.mask_Res1 = ResBlock(64, 64, 3, 1, 1)
        self.mask_Res2 = ResBlock(64, 64, 3, 1, 1)
        self.down1 = nn.Conv2d(64, 128, 3, 2, 1)
        self.mask_Res1_1d = ResBlock(128, 128, 3, 1, 1)
        self.mask_Res1_2d = ResBlock(128, 128, 3, 1, 1)
        self.down2 = nn.Conv2d(128, 256, 3, 2, 1)
        self.mask_Res2_1d = ResBlock(256, 256, 3, 1, 1)
        self.mask_Res2_2d = ResBlock(256, 256, 3, 1, 1)
        self.down3 = nn.Conv2d(256, 512, 3, 2, 1)
        self.mask_Res3_1d = ResBlock(512, 512, 3, 1, 1)
        self.mask_Res3_2d = ResBlock(512, 512, 3, 1, 1)
        self.up3 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res3_1u = ResBlock(512, 256, 3, 1, 1)
        self.mask_Res3_2u = ResBlock(256, 256, 3, 1, 1)
        self.up2 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res2_1u = ResBlock(256, 128, 3, 1, 1)
        self.mask_Res2_2u = ResBlock(128, 128, 3, 1, 1)
        self.up1 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res1_1u = ResBlock(128, 64, 3, 1, 1)
        self.mask_Res1_2u = ResBlock(64, 64, 3, 1, 1)
        self.mask_tail = nn.Conv2d(64, 26, 3, 1, 1)

    def forward(self, input):
        maskd0 = self.mask_Res2(self.mask_Res1(self.mask_head(input)))
        maskd1 = self.mask_Res1_2d(self.mask_Res1_1d(self.down1(maskd0)))
        maskd2 = self.mask_Res2_2d(self.mask_Res2_1d(self.down2(maskd1)))
        maskd3 = self.mask_Res3_2d(self.mask_Res3_1d(self.down3(maskd2)))
        masku2 = self.mask_Res3_2u(self.mask_Res3_1u(self.up3(maskd3))
            ) + maskd2
        masku1 = self.mask_Res2_2u(self.mask_Res2_1u(self.up2(masku2))
            ) + maskd1
        masku0 = self.mask_Res1_2u(self.mask_Res1_1u(self.up1(masku1))
            ) + maskd0
        mask = self.mask_tail(masku0)
        return mask


def get_inputs():
    return [torch.rand([4, 4, 8, 8])]


def get_init_inputs():
    return [[], {'input_channel': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
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


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_6(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 % 512
    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_7(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 % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_8(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.full([1], 0, tl.int64)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_9(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_10(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 2 % 2
    x0 = xindex % 2
    x5 = xindex // 4
    x2 = xindex // 4 % 512
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x5, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 1, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tl.where(tmp7, tmp6, tmp5)
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tl.where(tmp14, tmp13, tmp12)
    tmp16 = tmp11 - tmp11
    tmp18 = tmp16 * tmp17
    tmp19 = tmp11 + tmp18
    tmp21 = tmp20 + tmp1
    tmp22 = tmp20 < 0
    tl.where(tmp22, tmp21, tmp20)
    tmp24 = tmp19 - tmp19
    tmp26 = tmp24 * tmp25
    tmp27 = tmp19 + tmp26
    tl.store(in_out_ptr0 + x6, tmp27, None)


@triton.jit
def triton_poi_fused__to_copy_11(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_12(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.minimum(tmp8, tmp7)
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_13(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.3333333333333333
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_14(in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x6 = xindex // 16
    x2 = xindex // 16 % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 2, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.load(in_ptr4 + (tmp8 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp13 = tmp11 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp8 + 2 * tmp17 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = tl.load(in_ptr4 + (tmp8 + 2 * tmp17 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp21 = tmp19 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp25 + 2 * tmp17 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr4 + (tmp25 + 2 * tmp17 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 - tmp21
    tmp32 = tmp30 * tmp31
    tmp33 = tmp21 + tmp32
    tmp34 = tl.load(in_ptr2 + (tmp25 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp35 = tmp34 + tmp10
    tmp36 = tl.load(in_ptr4 + (tmp25 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 - tmp13
    tmp39 = tmp38 * tmp31
    tmp40 = tmp13 + tmp39
    tmp41 = tmp40 - tmp33
    tmp43 = tmp41 * tmp42
    tmp44 = tmp33 + tmp43
    tl.store(in_out_ptr1 + x4, tmp44, None)


@triton.jit
def triton_poi_fused__to_copy_15(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_16(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 3, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_17(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.42857142857142855
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_18(in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x6 = xindex // 64
    x2 = xindex // 64 % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.load(in_ptr4 + (tmp8 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp13 = tmp11 + tmp12
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp8 + 4 * tmp17 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = tl.load(in_ptr4 + (tmp8 + 4 * tmp17 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp21 = tmp19 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp25 + 4 * tmp17 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr4 + (tmp25 + 4 * tmp17 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 - tmp21
    tmp32 = tmp30 * tmp31
    tmp33 = tmp21 + tmp32
    tmp34 = tl.load(in_ptr2 + (tmp25 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp35 = tmp34 + tmp10
    tmp36 = tl.load(in_ptr4 + (tmp25 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 - tmp13
    tmp39 = tmp38 * tmp31
    tmp40 = tmp13 + tmp39
    tmp41 = tmp40 - tmp33
    tmp43 = tmp41 * tmp42
    tmp44 = tmp33 + tmp43
    tl.store(in_out_ptr1 + x4, tmp44, None)


@triton.jit
def triton_poi_fused_add_convolution_19(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_20(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 6656
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 26
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


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) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (128, 64, 3, 3), (576, 9, 3, 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, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (256,), (1,))
    assert_size_stride(primals_24, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_25, (256,), (1,))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256,), (1,))
    assert_size_stride(primals_28, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_29, (256,), (1,))
    assert_size_stride(primals_30, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_31, (256,), (1,))
    assert_size_stride(primals_32, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_33, (512,), (1,))
    assert_size_stride(primals_34, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (512,), (1,))
    assert_size_stride(primals_36, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_37, (512,), (1,))
    assert_size_stride(primals_38, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_39, (512,), (1,))
    assert_size_stride(primals_40, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (512,), (1,))
    assert_size_stride(primals_42, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_43, (256,), (1,))
    assert_size_stride(primals_44, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (256,), (1,))
    assert_size_stride(primals_46, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (256,), (1,))
    assert_size_stride(primals_48, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_49, (256,), (1,))
    assert_size_stride(primals_50, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_51, (128,), (1,))
    assert_size_stride(primals_52, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_53, (128,), (1,))
    assert_size_stride(primals_54, (128, 128, 3, 3), (1152, 9, 3, 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, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_59, (64,), (1,))
    assert_size_stride(primals_60, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (64,), (1,))
    assert_size_stride(primals_62, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_63, (64,), (1,))
    assert_size_stride(primals_64, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_65, (64,), (1,))
    assert_size_stride(primals_66, (26, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_67, (26,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16384)](buf1, primals_2, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(16384)](buf3, primals_5, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_0[grid(16384)](buf5, primals_7, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_1[grid(16384)](buf7, primals_9, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_0[grid(16384)](buf9, primals_11, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_2[grid(8192)](buf11, primals_13, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_3[grid(8192)](buf13, primals_15, 
            8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_15
        buf14 = extern_kernels.convolution(buf13, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_2[grid(8192)](buf15, primals_17, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_3[grid(8192)](buf17, primals_19, 
            8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_19
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_2[grid(8192)](buf19, primals_21, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
        buf20 = extern_kernels.convolution(buf19, primals_22, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_4[grid(4096)](buf21, primals_23, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_23
        buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf23 = buf22
        del buf22
        triton_poi_fused_convolution_relu_5[grid(4096)](buf23, primals_25, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_4[grid(4096)](buf25, primals_27, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_27
        buf26 = extern_kernels.convolution(buf25, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_5[grid(4096)](buf27, primals_29, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_29
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf29 = buf28
        del buf28
        triton_poi_fused_convolution_4[grid(4096)](buf29, primals_31, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_31
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 512, 1, 1), (512, 1, 1, 1))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_6[grid(2048)](buf31, primals_33, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_33
        buf32 = extern_kernels.convolution(buf31, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 1, 1), (512, 1, 1, 1))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_relu_7[grid(2048)](buf33, primals_35, 
            2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_35
        buf34 = extern_kernels.convolution(buf33, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 512, 1, 1), (512, 1, 1, 1))
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_6[grid(2048)](buf35, primals_37, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_37
        buf36 = extern_kernels.convolution(buf35, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 1, 1), (512, 1, 1, 1))
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_7[grid(2048)](buf37, primals_39, 
            2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_39
        buf38 = extern_kernels.convolution(buf37, primals_40, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 1, 1), (512, 1, 1, 1))
        buf39 = empty_strided_cuda((2, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_8[grid(2)](buf39, 2, XBLOCK=2, num_warps=
            1, num_stages=1)
        buf40 = empty_strided_cuda((2, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_8[grid(2)](buf40, 2, XBLOCK=2, num_warps=
            1, num_stages=1)
        buf41 = empty_strided_cuda((2,), (1,), torch.int64)
        triton_poi_fused__to_copy_8[grid(2)](buf41, 2, XBLOCK=2, num_warps=
            1, num_stages=1)
        buf42 = empty_strided_cuda((2,), (1,), torch.int64)
        triton_poi_fused__to_copy_8[grid(2)](buf42, 2, XBLOCK=2, num_warps=
            1, num_stages=1)
        buf43 = empty_strided_cuda((2,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_9[grid(2)](buf43, 2,
            XBLOCK=2, num_warps=1, num_stages=1)
        buf45 = empty_strided_cuda((2, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_9[grid(2)](buf45, 2,
            XBLOCK=2, num_warps=1, num_stages=1)
        buf44 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
            float32)
        buf46 = buf44
        del buf44
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_10[grid(8192)](
            buf46, buf39, buf41, buf38, primals_41, buf42, buf43, buf40,
            buf45, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del buf38
        del primals_41
        buf47 = extern_kernels.convolution(buf46, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf48 = buf47
        del buf47
        triton_poi_fused_convolution_relu_5[grid(4096)](buf48, primals_43, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_43
        buf49 = extern_kernels.convolution(buf48, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf50 = buf49
        del buf49
        triton_poi_fused_convolution_4[grid(4096)](buf50, primals_45, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_45
        buf51 = extern_kernels.convolution(buf50, primals_46, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf52 = buf51
        del buf51
        triton_poi_fused_convolution_relu_5[grid(4096)](buf52, primals_47, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_47
        buf53 = extern_kernels.convolution(buf52, primals_48, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 256, 2, 2), (1024, 4, 2, 1))
        buf54 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_11[grid(4)](buf54, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf55 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_12[grid(4)](buf55, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf56 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused__to_copy_11[grid(4)](buf56, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf57 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_add_clamp_12[grid(4)](buf57, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf60 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_13[grid(4)](buf60, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf62 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_13[grid(4)](buf62, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf59 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        buf63 = buf59
        del buf59
        buf64 = buf63
        del buf63
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_14[grid(16384)](
            buf64, buf55, buf56, buf53, primals_49, buf29, buf54, buf57,
            buf60, buf62, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf53
        del primals_49
        buf65 = extern_kernels.convolution(buf64, primals_50, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf66 = buf65
        del buf65
        triton_poi_fused_convolution_relu_3[grid(8192)](buf66, primals_51, 
            8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_51
        buf67 = extern_kernels.convolution(buf66, primals_52, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf68 = buf67
        del buf67
        triton_poi_fused_convolution_2[grid(8192)](buf68, primals_53, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_53
        buf69 = extern_kernels.convolution(buf68, primals_54, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf70 = buf69
        del buf69
        triton_poi_fused_convolution_relu_3[grid(8192)](buf70, primals_55, 
            8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_55
        buf71 = extern_kernels.convolution(buf70, primals_56, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf71, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf72 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_15[grid(8)](buf72, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf73 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_16[grid(8)](buf73, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf74 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_15[grid(8)](buf74, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf75 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused_add_clamp_16[grid(8)](buf75, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf78 = empty_strided_cuda((8,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_17[grid(8)](buf78, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf80 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_17[grid(8)](buf80, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf77 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        buf81 = buf77
        del buf77
        buf82 = buf81
        del buf81
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_18[grid(32768)](
            buf82, buf73, buf74, buf71, primals_57, buf19, buf72, buf75,
            buf78, buf80, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf71
        del primals_57
        buf83 = extern_kernels.convolution(buf82, primals_58, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf84 = buf83
        del buf83
        triton_poi_fused_convolution_relu_1[grid(16384)](buf84, primals_59,
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_59
        buf85 = extern_kernels.convolution(buf84, primals_60, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf85, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf86 = buf85
        del buf85
        triton_poi_fused_convolution_0[grid(16384)](buf86, primals_61, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_61
        buf87 = extern_kernels.convolution(buf86, primals_62, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf88 = buf87
        del buf87
        triton_poi_fused_convolution_relu_1[grid(16384)](buf88, primals_63,
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_63
        buf89 = extern_kernels.convolution(buf88, primals_64, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf89, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf90 = buf89
        del buf89
        triton_poi_fused_add_convolution_19[grid(16384)](buf90, primals_65,
            buf9, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_65
        buf91 = extern_kernels.convolution(buf90, primals_66, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf91, (4, 26, 8, 8), (1664, 64, 8, 1))
        buf92 = buf91
        del buf91
        triton_poi_fused_convolution_20[grid(6656)](buf92, primals_67, 6656,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_67
    return (buf92, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, primals_22, primals_24, primals_26, primals_28,
        primals_30, primals_32, primals_34, primals_36, primals_38,
        primals_40, primals_42, primals_44, primals_46, primals_48,
        primals_50, primals_52, primals_54, primals_56, primals_58,
        primals_60, primals_62, primals_64, primals_66, buf1, buf3, buf5,
        buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25,
        buf27, buf29, buf31, buf33, buf35, buf37, buf39, buf40, buf41,
        buf42, buf43, buf45, buf46, buf48, buf50, buf52, buf54, buf55,
        buf56, buf57, buf60, buf62, buf64, buf66, buf68, buf70, buf72,
        buf73, buf74, buf75, buf78, buf80, buf82, buf84, buf86, buf88, buf90)


class ResBlock(nn.Module):

    def __init__(self, in_channel, out_channel, ker_size, stri, pad):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channel, out_channel, 3, 1, 1)
        self.conv2 = nn.Conv2d(out_channel, out_channel, 3, 1, 1)

    def forward(self, x):
        return self.conv2(F.relu(self.conv1(x)))


class ada_maskNew(nn.Module):

    def __init__(self, input_channel):
        super(ada_maskNew, self).__init__()
        self.mask_head = nn.Conv2d(input_channel, 64, 3, 1, 1)
        self.mask_Res1 = ResBlock(64, 64, 3, 1, 1)
        self.mask_Res2 = ResBlock(64, 64, 3, 1, 1)
        self.down1 = nn.Conv2d(64, 128, 3, 2, 1)
        self.mask_Res1_1d = ResBlock(128, 128, 3, 1, 1)
        self.mask_Res1_2d = ResBlock(128, 128, 3, 1, 1)
        self.down2 = nn.Conv2d(128, 256, 3, 2, 1)
        self.mask_Res2_1d = ResBlock(256, 256, 3, 1, 1)
        self.mask_Res2_2d = ResBlock(256, 256, 3, 1, 1)
        self.down3 = nn.Conv2d(256, 512, 3, 2, 1)
        self.mask_Res3_1d = ResBlock(512, 512, 3, 1, 1)
        self.mask_Res3_2d = ResBlock(512, 512, 3, 1, 1)
        self.up3 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res3_1u = ResBlock(512, 256, 3, 1, 1)
        self.mask_Res3_2u = ResBlock(256, 256, 3, 1, 1)
        self.up2 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res2_1u = ResBlock(256, 128, 3, 1, 1)
        self.mask_Res2_2u = ResBlock(128, 128, 3, 1, 1)
        self.up1 = nn.UpsamplingBilinear2d(scale_factor=2)
        self.mask_Res1_1u = ResBlock(128, 64, 3, 1, 1)
        self.mask_Res1_2u = ResBlock(64, 64, 3, 1, 1)
        self.mask_tail = nn.Conv2d(64, 26, 3, 1, 1)

    def forward(self, input_0):
        primals_1 = self.mask_head.weight
        primals_2 = self.mask_head.bias
        primals_4 = self.mask_Res1.conv1.weight
        primals_5 = self.mask_Res1.conv1.bias
        primals_6 = self.mask_Res1.conv2.weight
        primals_7 = self.mask_Res1.conv2.bias
        primals_8 = self.mask_Res2.conv1.weight
        primals_9 = self.mask_Res2.conv1.bias
        primals_10 = self.mask_Res2.conv2.weight
        primals_11 = self.mask_Res2.conv2.bias
        primals_12 = self.down1.weight
        primals_13 = self.down1.bias
        primals_14 = self.mask_Res1_1d.conv1.weight
        primals_15 = self.mask_Res1_1d.conv1.bias
        primals_16 = self.mask_Res1_1d.conv2.weight
        primals_17 = self.mask_Res1_1d.conv2.bias
        primals_18 = self.mask_Res1_2d.conv1.weight
        primals_19 = self.mask_Res1_2d.conv1.bias
        primals_20 = self.mask_Res1_2d.conv2.weight
        primals_21 = self.mask_Res1_2d.conv2.bias
        primals_22 = self.down2.weight
        primals_23 = self.down2.bias
        primals_24 = self.mask_Res2_1d.conv1.weight
        primals_25 = self.mask_Res2_1d.conv1.bias
        primals_26 = self.mask_Res2_1d.conv2.weight
        primals_27 = self.mask_Res2_1d.conv2.bias
        primals_28 = self.mask_Res2_2d.conv1.weight
        primals_29 = self.mask_Res2_2d.conv1.bias
        primals_30 = self.mask_Res2_2d.conv2.weight
        primals_31 = self.mask_Res2_2d.conv2.bias
        primals_32 = self.down3.weight
        primals_33 = self.down3.bias
        primals_34 = self.mask_Res3_1d.conv1.weight
        primals_35 = self.mask_Res3_1d.conv1.bias
        primals_36 = self.mask_Res3_1d.conv2.weight
        primals_37 = self.mask_Res3_1d.conv2.bias
        primals_38 = self.mask_Res3_2d.conv1.weight
        primals_39 = self.mask_Res3_2d.conv1.bias
        primals_40 = self.mask_Res3_2d.conv2.weight
        primals_41 = self.mask_Res3_2d.conv2.bias
        primals_42 = self.mask_Res3_1u.conv1.weight
        primals_43 = self.mask_Res3_1u.conv1.bias
        primals_44 = self.mask_Res3_1u.conv2.weight
        primals_45 = self.mask_Res3_1u.conv2.bias
        primals_46 = self.mask_Res3_2u.conv1.weight
        primals_47 = self.mask_Res3_2u.conv1.bias
        primals_48 = self.mask_Res3_2u.conv2.weight
        primals_49 = self.mask_Res3_2u.conv2.bias
        primals_50 = self.mask_Res2_1u.conv1.weight
        primals_51 = self.mask_Res2_1u.conv1.bias
        primals_52 = self.mask_Res2_1u.conv2.weight
        primals_53 = self.mask_Res2_1u.conv2.bias
        primals_54 = self.mask_Res2_2u.conv1.weight
        primals_55 = self.mask_Res2_2u.conv1.bias
        primals_56 = self.mask_Res2_2u.conv2.weight
        primals_57 = self.mask_Res2_2u.conv2.bias
        primals_58 = self.mask_Res1_1u.conv1.weight
        primals_59 = self.mask_Res1_1u.conv1.bias
        primals_60 = self.mask_Res1_1u.conv2.weight
        primals_61 = self.mask_Res1_1u.conv2.bias
        primals_62 = self.mask_Res1_2u.conv1.weight
        primals_63 = self.mask_Res1_2u.conv1.bias
        primals_64 = self.mask_Res1_2u.conv2.weight
        primals_65 = self.mask_Res1_2u.conv2.bias
        primals_66 = self.mask_tail.weight
        primals_67 = self.mask_tail.bias
        primals_3 = 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])
        return output[0]
