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

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


# kernel path: runs/run_shard_6/inductor_cache/sj/csj6uus7z5hpvi77pvgp63jx4bne5i65mpzpsuvveo3mzfov6ycm.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = 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 = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_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_6/inductor_cache/3d/c3dyw6akmjhpp35jaqq3z7ozrdt7ywexn7u7aab2ekphhcccz3vm.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_5 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %relu_4], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 98304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 6
    x0 = xindex % 4096
    x2 = (xindex // 24576)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (12288*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 6, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-3) + x1)) + (12288*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-3) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x3), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ow/cownydwjrbxdahpvenvupljtyufltq67f2fj5gt5o4scl4zayria.py
# Topologically Sorted Source Nodes: [conv2d_5, x_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
#   x_6 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_12, %primals_13, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 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_6/inductor_cache/pm/cpmebq3upbvorlknkawxxg47exa2esmp72fr4oergswfas6nzozi.py
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_10 => convolution_9
# Graph fragment:
#   %convolution_9 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_8, %primals_20, %primals_21, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_6/inductor_cache/2w/c2wfghmr7ifgp5lwp4xbh6uilviyquukn5atkpgsuzqigf2neqhl.py
# Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_11 => convolution_10
# Graph fragment:
#   %convolution_10 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_9, %primals_22, %primals_23, [1, 1], [0, 0], [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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_6/inductor_cache/q3/cq3dhejhbflsq6b3s24hodc5wf6onjm5p2hkf2ioehmsukht5nqn.py
# Topologically Sorted Source Nodes: [conv2d_11, x_12], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   x_12 => relu_9
# 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_9 : [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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_6/inductor_cache/kd/ckdlwibiqaazskvdjvxwkd4indaw5sheqpx2sp4xc2kozraimzub.py
# Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_15 => convolution_14
# Graph fragment:
#   %convolution_14 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_11, %primals_30, %primals_31, [2, 2], [0, 0], [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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 512
    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_6/inductor_cache/fq/cfqx26sdcndsc5gatytwl4euxap46grc7jzzcn5mvscdw6nqn7df.py
# Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_16 => convolution_15
# Graph fragment:
#   %convolution_15 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_14, %primals_32, %primals_33, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_7 = async_compile.triton('triton_poi_fused_convolution_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 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_6/inductor_cache/l7/cl7bz6ohn2mrdkd6u6pn34feultswfgsfww5c4mlqufw3ceah7ms.py
# Topologically Sorted Source Nodes: [conv2d_16, x_17], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_16 => convolution_16
#   x_17 => relu_12
# 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_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_16,), kwargs = {})
triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 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_6/inductor_cache/a6/ca6awxcje2i7eam7k7hhbngb5arvbk47bz6rxrralf2ykykdcxne.py
# Topologically Sorted Source Nodes: [conv2d_22, x_23], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_22 => convolution_22
#   x_23 => relu_18
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_17, %primals_46, %primals_47, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_18 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_22,), kwargs = {})
triton_poi_fused_convolution_relu_9 = async_compile.triton('triton_poi_fused_convolution_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 512
    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_6/inductor_cache/xt/cxtft4hhlq5frjmrjznvv5fbmoy47cdz3ukveuea4agzx6kpgjf5.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_25 => add
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_23, %relu_11), kwargs = {})
triton_poi_fused_add_10 = async_compile.triton('triton_poi_fused_add_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (x0), None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/lv/clvbt33fgl6tz5gt2yhczjt3vfnqg42hfa6zzs2n22qa2ou2lm4j.py
# Topologically Sorted Source Nodes: [conv2d_25, x_28], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_25 => convolution_26
#   x_28 => relu_21
# Graph fragment:
#   %convolution_26 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_20, %primals_53, %primals_54, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_21 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_26,), kwargs = {})
triton_poi_fused_convolution_relu_11 = async_compile.triton('triton_poi_fused_convolution_relu_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_6/inductor_cache/y6/cy6gjd2h3soz3je2nnahvlj34xy2he6ak4idn7hv34stk63ito3c.py
# Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_30 => add_1
# Graph fragment:
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_27, %relu_8), kwargs = {})
triton_poi_fused_add_12 = async_compile.triton('triton_poi_fused_add_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_12(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (x0), None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/i5/ci5nruo7op4b4ryylrbr6xopw5avdv6wbkjcclosup2d6uh37r6e.py
# Topologically Sorted Source Nodes: [x_33, y, clamp], Original ATen: [aten.convolution, aten.add, aten.clamp, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
#   clamp => clamp_max, clamp_min
#   x_33 => convolution_30
#   y => add_2
# Graph fragment:
#   %convolution_30 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_23, %primals_60, %primals_61, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %convolution_30), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_2, 0.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_2, 0.0), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add_2, 1.0), kwargs = {})
#   %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le), kwargs = {})
triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13 = async_compile.triton('triton_poi_fused_add_clamp_convolution_ge_le_logical_and_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 49152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 3
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x3), None)
    tmp2 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = 1.0
    tmp8 = triton_helpers.minimum(tmp6, tmp7)
    tmp9 = tmp4 >= tmp5
    tmp10 = tmp4 <= tmp7
    tmp11 = tmp9 & tmp10
    tl.store(out_ptr0 + (x3), tmp8, None)
    tl.store(out_ptr1 + (x3), tmp11, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pz/cpz2mf53nsbgomqucnhnmpjmugy2wxofag7ddkjo3uawvsnktnsj.py
# Topologically Sorted Source Nodes: [conv2d_4, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   x_4 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [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_4,), kwargs = {})
#   %le_20 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 49152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 3
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, 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 = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (3, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (3, ), (1, ))
    assert_size_stride(primals_12, (64, 6, 3, 3), (54, 9, 3, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64, ), (1, ))
    assert_size_stride(primals_18, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_19, (64, ), (1, ))
    assert_size_stride(primals_20, (256, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_21, (256, ), (1, ))
    assert_size_stride(primals_22, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128, ), (1, ))
    assert_size_stride(primals_28, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_29, (128, ), (1, ))
    assert_size_stride(primals_30, (512, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_31, (512, ), (1, ))
    assert_size_stride(primals_32, (256, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_33, (256, ), (1, ))
    assert_size_stride(primals_34, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (256, ), (1, ))
    assert_size_stride(primals_36, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (256, ), (1, ))
    assert_size_stride(primals_38, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (256, ), (1, ))
    assert_size_stride(primals_40, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_41, (256, ), (1, ))
    assert_size_stride(primals_42, (256, 256, 3, 3), (2304, 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, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (512, ), (1, ))
    assert_size_stride(primals_48, (512, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_49, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_50, (128, ), (1, ))
    assert_size_stride(primals_51, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_52, (128, ), (1, ))
    assert_size_stride(primals_53, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_54, (256, ), (1, ))
    assert_size_stride(primals_55, (256, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_56, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_57, (64, ), (1, ))
    assert_size_stride(primals_58, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_59, (64, ), (1, ))
    assert_size_stride(primals_60, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (3, ), (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, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 524288, grid=grid(524288), 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 524288, grid=grid(524288), 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf5, primals_7, 524288, grid=grid(524288), 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf7, primals_9, 524288, grid=grid(524288), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [conv2d_4], 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, 3, 64, 64), (12288, 4096, 64, 1))
        buf9 = empty_strided_cuda((4, 6, 64, 64), (24576, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_3, buf8, primals_11, buf9, 98304, grid=grid(98304), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, x_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf11, primals_13, 1048576, grid=grid(1048576), 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf13, primals_15, 1048576, grid=grid(1048576), 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, x_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf15, primals_17, 1048576, grid=grid(1048576), 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, x_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf17, primals_19, 1048576, grid=grid(1048576), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 256, 32, 32), (262144, 1024, 32, 1))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf19, primals_21, 1048576, grid=grid(1048576), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf21, primals_23, 524288, grid=grid(524288), 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, x_12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf23, primals_25, 524288, grid=grid(524288), 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12, x_13], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf25, primals_27, 524288, grid=grid(524288), 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, x_14], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf27, primals_29, 524288, grid=grid(524288), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf29 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf29, primals_31, 524288, grid=grid(524288), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf31, primals_33, 262144, grid=grid(262144), 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, 256, 16, 16), (65536, 256, 16, 1))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16, x_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf33, primals_35, 262144, grid=grid(262144), 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, 256, 16, 16), (65536, 256, 16, 1))
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17, x_18], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf35, primals_37, 262144, grid=grid(262144), 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, 256, 16, 16), (65536, 256, 16, 1))
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_18, x_19], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf37, primals_39, 262144, grid=grid(262144), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [conv2d_19], 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, 256, 16, 16), (65536, 256, 16, 1))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [conv2d_19, x_20], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf39, primals_41, 262144, grid=grid(262144), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf41 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [conv2d_20, x_21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf41, primals_43, 262144, grid=grid(262144), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf43 = buf42; del buf42  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21, x_22], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf43, primals_45, 262144, grid=grid(262144), stream=stream0)
        del primals_45
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf43, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf45 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22, x_23], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf45, primals_47, 524288, grid=grid(524288), stream=stream0)
        del primals_47
        # Topologically Sorted Source Nodes: [x_24], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_48, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf47 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.add]
        triton_poi_fused_add_10.run(buf47, buf27, 524288, grid=grid(524288), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_23], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_49, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [conv2d_23, x_26], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf49, primals_50, 524288, grid=grid(524288), stream=stream0)
        del primals_50
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        buf50 = extern_kernels.convolution(buf49, primals_51, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf51 = buf50; del buf50  # reuse
        # Topologically Sorted Source Nodes: [conv2d_24, x_27], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf51, primals_52, 524288, grid=grid(524288), stream=stream0)
        del primals_52
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        buf52 = extern_kernels.convolution(buf51, primals_53, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 256, 32, 32), (262144, 1024, 32, 1))
        buf53 = buf52; del buf52  # reuse
        # Topologically Sorted Source Nodes: [conv2d_25, x_28], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_11.run(buf53, primals_54, 1048576, grid=grid(1048576), stream=stream0)
        del primals_54
        # Topologically Sorted Source Nodes: [x_29], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_55, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf55 = buf54; del buf54  # reuse
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.add]
        triton_poi_fused_add_12.run(buf55, buf17, 1048576, grid=grid(1048576), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_26], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_56, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf57 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [conv2d_26, x_31], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf57, primals_57, 1048576, grid=grid(1048576), stream=stream0)
        del primals_57
        # Topologically Sorted Source Nodes: [conv2d_27], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, primals_58, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf59 = buf58; del buf58  # reuse
        # Topologically Sorted Source Nodes: [conv2d_27, x_32], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf59, primals_59, 1048576, grid=grid(1048576), stream=stream0)
        del primals_59
        # Topologically Sorted Source Nodes: [x_33], Original ATen: [aten.convolution]
        buf60 = extern_kernels.convolution(buf59, primals_60, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf60, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf61 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.float32)
        buf62 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_33, y, clamp], Original ATen: [aten.convolution, aten.add, aten.clamp, aten.ge, aten.le, aten.logical_and]
        triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13.run(primals_3, buf60, primals_61, buf61, buf62, 49152, grid=grid(49152), stream=stream0)
        del buf60
        del primals_61
        buf63 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_4, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_14.run(buf8, primals_11, buf63, 49152, grid=grid(49152), stream=stream0)
        del buf8
        del primals_11
    return (buf61, 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_49, primals_51, primals_53, primals_55, primals_56, primals_58, primals_60, buf1, buf3, buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf29, buf31, buf33, buf35, buf37, buf39, buf41, buf43, buf45, buf47, buf49, buf51, buf53, buf55, buf57, buf59, buf62, buf63, )


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((32, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((3, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 6, 3, 3), (54, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((256, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((512, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((256, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((256, 256, 3, 3), (2304, 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((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((512, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((256, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((64, 64, 3, 3), (576, 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((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61])
    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


class ImageCleanModel(nn.Module):
    """ImageClean Model."""

    def __init__(self):
        """Init model."""
        super(ImageCleanModel, self).__init__()
        self.relu = nn.ReLU(inplace=True)
        self.E01 = nn.Conv2d(3, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E02 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E03 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E04 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E05 = nn.Conv2d(32, 3, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.DS01_layer00 = nn.Conv2d(6, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer01 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer02 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer03 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS02 = nn.Conv2d(64, 256, kernel_size=[2, 2], stride=(2, 2))
        self.DS02_layer00_cf = nn.Conv2d(256, 128, kernel_size=[1, 1],
            stride=(1, 1))
        self.DS02_layer00 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS02_layer01 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS02_layer02 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03 = nn.Conv2d(128, 512, kernel_size=[2, 2], stride=(2, 2))
        self.DS03_layer00_cf = nn.Conv2d(512, 256, kernel_size=[1, 1],
            stride=(1, 1))
        self.DS03_layer00 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03_layer01 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03_layer02 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.UPS03_layer00 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer01 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer02 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer03 = nn.Conv2d(256, 512, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.USP02 = nn.ConvTranspose2d(512, 128, kernel_size=[2, 2],
            stride=(2, 2), bias=False)
        self.US02_layer00 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.US02_layer01 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.US02_layer02 = nn.Conv2d(128, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.USP01 = nn.ConvTranspose2d(256, 64, kernel_size=[2, 2], stride
            =(2, 2), bias=False)
        self.US01_layer00 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.US01_layer01 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.US01_layer02 = nn.Conv2d(64, 3, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))

    def forward(self, input):
        x = self.relu(self.E01(input))
        x = self.relu(self.E02(x))
        x = self.relu(self.E03(x))
        x = self.relu(self.E04(x))
        x = self.relu(self.E05(x))
        noise_level = x
        x = torch.cat((input, noise_level), dim=1)
        x = self.relu(self.DS01_layer00(x))
        x = self.relu(self.DS01_layer01(x))
        x = self.relu(self.DS01_layer02(x))
        x = self.relu(self.DS01_layer03(x))
        down1_result = x
        x = self.DS02(down1_result)
        x = self.DS02_layer00_cf(x)
        x = self.relu(self.DS02_layer00(x))
        x = self.relu(self.DS02_layer01(x))
        x = self.relu(self.DS02_layer02(x))
        down2_result = x
        x = self.DS03(down2_result)
        x = self.DS03_layer00_cf(x)
        x = self.relu(self.DS03_layer00(x))
        x = self.relu(self.DS03_layer01(x))
        x = self.relu(self.DS03_layer02(x))
        x = self.relu(self.UPS03_layer00(x))
        x = self.relu(self.UPS03_layer01(x))
        x = self.relu(self.UPS03_layer02(x))
        x = self.relu(self.UPS03_layer03(x))
        x = self.USP02(x)
        x = torch.add(x, down2_result, alpha=1)
        del down2_result
        x = self.relu(self.US02_layer00(x))
        x = self.relu(self.US02_layer01(x))
        x = self.relu(self.US02_layer02(x))
        x = self.USP01(x)
        x = torch.add(x, down1_result, alpha=1)
        del down1_result
        x = self.relu(self.US01_layer00(x))
        x = self.relu(self.US01_layer01(x))
        x = self.US01_layer02(x)
        y = torch.add(input, x, alpha=1)
        del x
        return y.clamp(0.0, 1.0)


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_relu_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 // 4096 % 32
    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_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 6
    x0 = xindex % 4096
    x2 = xindex // 24576
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 12288 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 6, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-3 + x1) + 12288 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-3 + x1), tmp6, eviction_policy='evict_last',
        other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x3, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_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 // 4096 % 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_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 // 1024 % 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_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 // 1024 % 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_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 // 1024 % 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_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)
    x3 = xindex
    x1 = xindex // 256 % 512
    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_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)
    x3 = xindex
    x1 = xindex // 256 % 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_8(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 // 256 % 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_relu_9(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 // 256 % 512
    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_add_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 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_add_12(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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x3, None)
    tmp2 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = 1.0
    tmp8 = triton_helpers.minimum(tmp6, tmp7)
    tmp9 = tmp4 >= tmp5
    tmp10 = tmp4 <= tmp7
    tmp11 = tmp9 & tmp10
    tl.store(out_ptr0 + x3, tmp8, None)
    tl.store(out_ptr1 + x3, tmp11, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 3
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, 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) = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (3, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (3,), (1,))
    assert_size_stride(primals_12, (64, 6, 3, 3), (54, 9, 3, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_17, (64,), (1,))
    assert_size_stride(primals_18, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_19, (64,), (1,))
    assert_size_stride(primals_20, (256, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_21, (256,), (1,))
    assert_size_stride(primals_22, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_29, (128,), (1,))
    assert_size_stride(primals_30, (512, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_31, (512,), (1,))
    assert_size_stride(primals_32, (256, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_33, (256,), (1,))
    assert_size_stride(primals_34, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (256,), (1,))
    assert_size_stride(primals_36, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (256,), (1,))
    assert_size_stride(primals_38, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_39, (256,), (1,))
    assert_size_stride(primals_40, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_41, (256,), (1,))
    assert_size_stride(primals_42, (256, 256, 3, 3), (2304, 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, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (512,), (1,))
    assert_size_stride(primals_48, (512, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_49, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_50, (128,), (1,))
    assert_size_stride(primals_51, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_52, (128,), (1,))
    assert_size_stride(primals_53, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_54, (256,), (1,))
    assert_size_stride(primals_55, (256, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_56, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_57, (64,), (1,))
    assert_size_stride(primals_58, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_59, (64,), (1,))
    assert_size_stride(primals_60, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_61, (3,), (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, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(524288)](buf1, primals_2, 
            524288, XBLOCK=512, num_warps=8, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(524288)](buf3, primals_5, 
            524288, XBLOCK=512, num_warps=8, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_0[grid(524288)](buf5, primals_7, 
            524288, XBLOCK=512, num_warps=8, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_0[grid(524288)](buf7, primals_9, 
            524288, XBLOCK=512, num_warps=8, 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, 3, 64, 64), (12288, 4096, 64, 1))
        buf9 = empty_strided_cuda((4, 6, 64, 64), (24576, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(98304)](primals_3, buf8, primals_11,
            buf9, 98304, XBLOCK=512, num_warps=8, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf11,
            primals_13, 1048576, XBLOCK=1024, 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf13,
            primals_15, 1048576, XBLOCK=1024, 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf15,
            primals_17, 1048576, XBLOCK=1024, 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf17,
            primals_19, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_19
        buf18 = extern_kernels.convolution(buf17, primals_20, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 256, 32, 32), (262144, 1024, 32, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_3[grid(1048576)](buf19, primals_21, 
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_21
        buf20 = extern_kernels.convolution(buf19, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_4[grid(524288)](buf21, primals_23, 
            524288, XBLOCK=1024, 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf23 = buf22
        del buf22
        triton_poi_fused_convolution_relu_5[grid(524288)](buf23, primals_25,
            524288, XBLOCK=1024, 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_5[grid(524288)](buf25, primals_27,
            524288, XBLOCK=1024, 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_5[grid(524288)](buf27, primals_29,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_29
        buf28 = extern_kernels.convolution(buf27, primals_30, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf29 = buf28
        del buf28
        triton_poi_fused_convolution_6[grid(524288)](buf29, primals_31, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_31
        buf30 = extern_kernels.convolution(buf29, primals_32, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_7[grid(262144)](buf31, primals_33, 
            262144, XBLOCK=1024, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_relu_8[grid(262144)](buf33, primals_35,
            262144, XBLOCK=1024, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_relu_8[grid(262144)](buf35, primals_37,
            262144, XBLOCK=1024, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_8[grid(262144)](buf37, primals_39,
            262144, XBLOCK=1024, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_relu_8[grid(262144)](buf39, primals_41,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_41
        buf40 = extern_kernels.convolution(buf39, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf41 = buf40
        del buf40
        triton_poi_fused_convolution_relu_8[grid(262144)](buf41, primals_43,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_43
        buf42 = extern_kernels.convolution(buf41, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf43 = buf42
        del buf42
        triton_poi_fused_convolution_relu_8[grid(262144)](buf43, primals_45,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_45
        buf44 = extern_kernels.convolution(buf43, primals_46, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf45 = buf44
        del buf44
        triton_poi_fused_convolution_relu_9[grid(524288)](buf45, primals_47,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_47
        buf46 = extern_kernels.convolution(buf45, primals_48, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf47 = buf46
        del buf46
        triton_poi_fused_add_10[grid(524288)](buf47, buf27, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf48 = extern_kernels.convolution(buf47, primals_49, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf49 = buf48
        del buf48
        triton_poi_fused_convolution_relu_5[grid(524288)](buf49, primals_50,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_50
        buf50 = extern_kernels.convolution(buf49, primals_51, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf51 = buf50
        del buf50
        triton_poi_fused_convolution_relu_5[grid(524288)](buf51, primals_52,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_52
        buf52 = extern_kernels.convolution(buf51, primals_53, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 256, 32, 32), (262144, 1024, 32, 1))
        buf53 = buf52
        del buf52
        triton_poi_fused_convolution_relu_11[grid(1048576)](buf53,
            primals_54, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_54
        buf54 = extern_kernels.convolution(buf53, primals_55, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf55 = buf54
        del buf54
        triton_poi_fused_add_12[grid(1048576)](buf55, buf17, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf56 = extern_kernels.convolution(buf55, primals_56, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf57 = buf56
        del buf56
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf57,
            primals_57, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_57
        buf58 = extern_kernels.convolution(buf57, primals_58, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf59 = buf58
        del buf58
        triton_poi_fused_convolution_relu_2[grid(1048576)](buf59,
            primals_59, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_59
        buf60 = extern_kernels.convolution(buf59, primals_60, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf60, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf61 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        buf62 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_clamp_convolution_ge_le_logical_and_13[grid(49152)
            ](primals_3, buf60, primals_61, buf61, buf62, 49152, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf60
        del primals_61
        buf63 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_14[grid(49152)](
            buf8, primals_11, buf63, 49152, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf8
        del primals_11
    return (buf61, 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_49, primals_51, primals_53, primals_55, primals_56,
        primals_58, primals_60, buf1, buf3, buf5, buf7, buf9, buf11, buf13,
        buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf29, buf31,
        buf33, buf35, buf37, buf39, buf41, buf43, buf45, buf47, buf49,
        buf51, buf53, buf55, buf57, buf59, buf62, buf63)


class ImageCleanModelNew(nn.Module):
    """ImageClean Model."""

    def __init__(self):
        """Init model."""
        super(ImageCleanModelNew, self).__init__()
        self.relu = nn.ReLU(inplace=True)
        self.E01 = nn.Conv2d(3, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E02 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E03 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E04 = nn.Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.E05 = nn.Conv2d(32, 3, kernel_size=[3, 3], stride=(1, 1),
            padding=(1, 1))
        self.DS01_layer00 = nn.Conv2d(6, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer01 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer02 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS01_layer03 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.DS02 = nn.Conv2d(64, 256, kernel_size=[2, 2], stride=(2, 2))
        self.DS02_layer00_cf = nn.Conv2d(256, 128, kernel_size=[1, 1],
            stride=(1, 1))
        self.DS02_layer00 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS02_layer01 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS02_layer02 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03 = nn.Conv2d(128, 512, kernel_size=[2, 2], stride=(2, 2))
        self.DS03_layer00_cf = nn.Conv2d(512, 256, kernel_size=[1, 1],
            stride=(1, 1))
        self.DS03_layer00 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03_layer01 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.DS03_layer02 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.UPS03_layer00 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer01 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer02 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.UPS03_layer03 = nn.Conv2d(256, 512, kernel_size=[3, 3], stride
            =(1, 1), padding=(1, 1))
        self.USP02 = nn.ConvTranspose2d(512, 128, kernel_size=[2, 2],
            stride=(2, 2), bias=False)
        self.US02_layer00 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.US02_layer01 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.US02_layer02 = nn.Conv2d(128, 256, kernel_size=[3, 3], stride=
            (1, 1), padding=(1, 1))
        self.USP01 = nn.ConvTranspose2d(256, 64, kernel_size=[2, 2], stride
            =(2, 2), bias=False)
        self.US01_layer00 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.US01_layer01 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))
        self.US01_layer02 = nn.Conv2d(64, 3, kernel_size=[3, 3], stride=(1,
            1), padding=(1, 1))

    def forward(self, input_0):
        primals_1 = self.E01.weight
        primals_2 = self.E01.bias
        primals_4 = self.E02.weight
        primals_5 = self.E02.bias
        primals_6 = self.E03.weight
        primals_7 = self.E03.bias
        primals_8 = self.E04.weight
        primals_9 = self.E04.bias
        primals_10 = self.E05.weight
        primals_11 = self.E05.bias
        primals_12 = self.DS01_layer00.weight
        primals_13 = self.DS01_layer00.bias
        primals_14 = self.DS01_layer01.weight
        primals_15 = self.DS01_layer01.bias
        primals_16 = self.DS01_layer02.weight
        primals_17 = self.DS01_layer02.bias
        primals_18 = self.DS01_layer03.weight
        primals_19 = self.DS01_layer03.bias
        primals_20 = self.DS02.weight
        primals_21 = self.DS02.bias
        primals_22 = self.DS02_layer00_cf.weight
        primals_23 = self.DS02_layer00_cf.bias
        primals_24 = self.DS02_layer00.weight
        primals_25 = self.DS02_layer00.bias
        primals_26 = self.DS02_layer01.weight
        primals_27 = self.DS02_layer01.bias
        primals_28 = self.DS02_layer02.weight
        primals_29 = self.DS02_layer02.bias
        primals_30 = self.DS03.weight
        primals_31 = self.DS03.bias
        primals_32 = self.DS03_layer00_cf.weight
        primals_33 = self.DS03_layer00_cf.bias
        primals_34 = self.DS03_layer00.weight
        primals_35 = self.DS03_layer00.bias
        primals_36 = self.DS03_layer01.weight
        primals_37 = self.DS03_layer01.bias
        primals_38 = self.DS03_layer02.weight
        primals_39 = self.DS03_layer02.bias
        primals_40 = self.UPS03_layer00.weight
        primals_41 = self.UPS03_layer00.bias
        primals_42 = self.UPS03_layer01.weight
        primals_43 = self.UPS03_layer01.bias
        primals_44 = self.UPS03_layer02.weight
        primals_45 = self.UPS03_layer02.bias
        primals_46 = self.UPS03_layer03.weight
        primals_47 = self.UPS03_layer03.bias
        primals_48 = self.USP02.weight
        primals_49 = self.US02_layer00.weight
        primals_50 = self.US02_layer00.bias
        primals_51 = self.US02_layer01.weight
        primals_52 = self.US02_layer01.bias
        primals_53 = self.US02_layer02.weight
        primals_54 = self.US02_layer02.bias
        primals_55 = self.USP01.weight
        primals_56 = self.US01_layer00.weight
        primals_57 = self.US01_layer00.bias
        primals_58 = self.US01_layer01.weight
        primals_59 = self.US01_layer01.bias
        primals_60 = self.US01_layer02.weight
        primals_61 = self.US01_layer02.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])
        return output[0]
