# 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_7/inductor_cache/lp/clp5td7lbqtje3pt7v6xbcp766swgazqemomz2nzsxtdtmjesxht.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => 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=[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_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 = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 16
    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_7/inductor_cache/wy/cwyx3wa4jndgnwzcjpr33hhlviahccyeckxfax46ztwjbjc22gd7.py
# Topologically Sorted Source Nodes: [x1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x1 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/j6/cj6faeofhfnxsh5iuwazughjlau4igyajnmvjequyelq7apzs4qm.py
# Topologically Sorted Source Nodes: [x1_1, x1_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x1_1 => convolution_2
#   x1_2 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), 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=[131072], 
    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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_7/inductor_cache/6y/c6yx6oq7oo2cwoaop3iwu5iqfdckg6lycdtu4jjuiv3wdcf2o6p7.py
# Topologically Sorted Source Nodes: [x2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x2 => getitem_2, getitem_3
# Graph fragment:
#   %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/d4/cd4s5ogbgu46xbdaa3oicwxi7l6pnddrap26pxiqzcpei77ta53h.py
# Topologically Sorted Source Nodes: [x2_1, x2_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x2_1 => convolution_4
#   x2_2 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %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 = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_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_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 64
    tmp0 = tl.load(in_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_7/inductor_cache/a4/ca43wvja2n3mesrfuj54dcwx324bk23dhpnatmpi7kjryanvrx2z.py
# Topologically Sorted Source Nodes: [x3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x3 => getitem_4, getitem_5
# Graph fragment:
#   %getitem_4 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 0), kwargs = {})
#   %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xm/cxmz3iknyzchppdbcm7wr6eaxxzpoahax5jalpj6fh3xvvzjnqzl.py
# Topologically Sorted Source Nodes: [x3_1, x3_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x3_1 => convolution_6
#   x3_2 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
triton_poi_fused_convolution_relu_6 = async_compile.triton('triton_poi_fused_convolution_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    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_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_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    tmp0 = tl.load(in_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_7/inductor_cache/f7/cf7o7wnf37yxmpneyxpuue2wp32cye5lrxh45re7d5apq2tbhhro.py
# Topologically Sorted Source Nodes: [x4], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x4 => getitem_6, getitem_7
# Graph fragment:
#   %getitem_6 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 0), kwargs = {})
#   %getitem_7 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nn/cnnnq4kko5fnwaaj3dyc2icds7tzzfma73fo5puklebufk7h45d3.py
# Topologically Sorted Source Nodes: [x4_1, x4_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x4_1 => convolution_8
#   x4_2 => relu_8
# Graph fragment:
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_18, %primals_19, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), 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=[16384], 
    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 = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 256
    tmp0 = tl.load(in_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_7/inductor_cache/vt/cvtkhpy3oujzvltizkheo3m2lfdt7tarcd6iuke6ssvpf4d4jpmv.py
# Topologically Sorted Source Nodes: [x5_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x5_1 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_10, %relu_7], 1), kwargs = {})
triton_poi_fused_cat_9 = async_compile.triton('triton_poi_fused_cat_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=[65536], 
    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_9', '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_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 256
    x0 = xindex % 64
    x2 = (xindex // 16384)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x1) + (8192*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 256, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (64*((-128) + x1)) + (8192*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ss/cssj4vemnjgzdwfpoowvbqjaebujsbqhslet4ughio2qw3spidob.py
# Topologically Sorted Source Nodes: [x6_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x6_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_13, %relu_5], 1), kwargs = {})
triton_poi_fused_cat_10 = async_compile.triton('triton_poi_fused_cat_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=[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_10', '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_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 256) % 128
    x0 = xindex % 256
    x2 = (xindex // 32768)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (256*x1) + (16384*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 128, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (256*((-64) + x1)) + (16384*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fb/cfbh7kxk5krx3bvvif6ukfongvvxoiilpn4l7limujdttm6x4uzt.py
# Topologically Sorted Source Nodes: [x7_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x7_1 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_16, %relu_3], 1), kwargs = {})
triton_poi_fused_cat_11 = async_compile.triton('triton_poi_fused_cat_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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_11', '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_11(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 1024) % 64
    x0 = xindex % 1024
    x2 = (xindex // 65536)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (1024*x1) + (32768*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 64, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (1024*((-32) + x1)) + (32768*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/p5/cp54phyfpmyfhebtgeoqahl3sisnla77wis33pm6mlnou73sl5pt.py
# Topologically Sorted Source Nodes: [x8_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x8_1 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_19, %relu_1], 1), kwargs = {})
triton_poi_fused_cat_12 = async_compile.triton('triton_poi_fused_cat_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=[524288], 
    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_12', '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_12(in_ptr0, in_ptr1, in_ptr2, out_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)
    x1 = (xindex // 4096) % 32
    x0 = xindex % 4096
    x2 = (xindex // 131072)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 32, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x0 + (4096*((-16) + x1)) + (65536*x2)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ix/cix44y3v3fkksa6cnl4khok3j6bewjm6boxrmzn2xswlskl5fpbk.py
# Topologically Sorted Source Nodes: [outputs, out], Original ATen: [aten.convolution, aten.sigmoid]
# Source node to ATen node mapping:
#   out => sigmoid
#   outputs => convolution_22
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_17, %primals_46, %primals_47, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_22,), kwargs = {})
triton_poi_fused_convolution_sigmoid_13 = async_compile.triton('triton_poi_fused_convolution_sigmoid_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=[16384], 
    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_sigmoid_13', '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_sigmoid_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + (x0), tmp4, 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 = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 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, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (64, ), (1, ))
    assert_size_stride(primals_12, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (256, ), (1, ))
    assert_size_stride(primals_20, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_21, (256, ), (1, ))
    assert_size_stride(primals_22, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, 256, 3, 3), (2304, 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, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_29, (64, ), (1, ))
    assert_size_stride(primals_30, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (64, ), (1, ))
    assert_size_stride(primals_32, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_33, (64, ), (1, ))
    assert_size_stride(primals_34, (64, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_35, (32, ), (1, ))
    assert_size_stride(primals_36, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_37, (32, ), (1, ))
    assert_size_stride(primals_38, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_39, (32, ), (1, ))
    assert_size_stride(primals_40, (32, 16, 2, 2), (64, 4, 2, 1))
    assert_size_stride(primals_41, (16, ), (1, ))
    assert_size_stride(primals_42, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_43, (16, ), (1, ))
    assert_size_stride(primals_44, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_45, (16, ), (1, ))
    assert_size_stride(primals_46, (1, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_47, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], 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, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 262144, grid=grid(262144), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_3], 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, 16, 64, 64), (65536, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 262144, grid=grid(262144), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [x1_1], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, 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, 32, 32), (32768, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x1_1, x1_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf7, primals_7, 131072, grid=grid(131072), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x1_4], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x1_4, x1_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf9, primals_9, 131072, grid=grid(131072), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf9, buf10, buf11, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [x2_1], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, primals_10, 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, 16, 16), (16384, 256, 16, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [x2_1, x2_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf13, primals_11, 65536, grid=grid(65536), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [x2_4], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_12, 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, 16, 16), (16384, 256, 16, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [x2_4, x2_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf15, primals_13, 65536, grid=grid(65536), stream=stream0)
        del primals_13
        buf16 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf15, buf16, buf17, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [x3_1], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [x3_1, x3_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf19, primals_15, 32768, grid=grid(32768), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [x3_4], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [x3_4, x3_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf21, primals_17, 32768, grid=grid(32768), stream=stream0)
        del primals_17
        buf22 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x4], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf21, buf22, buf23, 8192, grid=grid(8192), stream=stream0)
        # Topologically Sorted Source Nodes: [x4_1], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf22, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [x4_1, x4_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf25, primals_19, 16384, grid=grid(16384), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x4_4], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [x4_4, x4_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf27, primals_21, 16384, grid=grid(16384), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [x5], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_22, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x5_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_9.run(buf28, primals_23, buf21, buf29, 65536, grid=grid(65536), stream=stream0)
        del buf28
        del primals_23
        # Topologically Sorted Source Nodes: [x5_2], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [x5_2, x5_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf31, primals_25, 32768, grid=grid(32768), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [x5_5], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [x5_5, x5_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf33, primals_27, 32768, grid=grid(32768), stream=stream0)
        del primals_27
        # Topologically Sorted Source Nodes: [x6], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, primals_28, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x6_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_10.run(buf34, primals_29, buf15, buf35, 131072, grid=grid(131072), stream=stream0)
        del buf34
        del primals_29
        # Topologically Sorted Source Nodes: [x6_2], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [x6_2, x6_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf37, primals_31, 65536, grid=grid(65536), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [x6_5], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [x6_5, x6_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf39, primals_33, 65536, grid=grid(65536), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [x7], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_34, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf41 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x7_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_11.run(buf40, primals_35, buf9, buf41, 262144, grid=grid(262144), stream=stream0)
        del buf40
        del primals_35
        # Topologically Sorted Source Nodes: [x7_2], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf43 = buf42; del buf42  # reuse
        # Topologically Sorted Source Nodes: [x7_2, x7_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf43, primals_37, 131072, grid=grid(131072), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [x7_5], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf43, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf45 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [x7_5, x7_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf45, primals_39, 131072, grid=grid(131072), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [x8], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_40, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf47 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x8_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_12.run(buf46, primals_41, buf3, buf47, 524288, grid=grid(524288), stream=stream0)
        del buf46
        del primals_41
        # Topologically Sorted Source Nodes: [x8_2], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [x8_2, x8_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf49, primals_43, 262144, grid=grid(262144), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [x8_5], Original ATen: [aten.convolution]
        buf50 = extern_kernels.convolution(buf49, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf51 = buf50; del buf50  # reuse
        # Topologically Sorted Source Nodes: [x8_5, x8_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf51, primals_45, 262144, grid=grid(262144), stream=stream0)
        del primals_45
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.convolution]
        buf52 = extern_kernels.convolution(buf51, primals_46, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf53 = buf52; del buf52  # reuse
        # Topologically Sorted Source Nodes: [outputs, out], Original ATen: [aten.convolution, aten.sigmoid]
        triton_poi_fused_convolution_sigmoid_13.run(buf53, primals_47, 16384, grid=grid(16384), stream=stream0)
        del primals_47
    return (buf53, 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, buf1, buf3, buf4, buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf29, buf31, buf33, buf35, buf37, buf39, buf41, buf43, buf45, buf47, buf49, buf51, buf53, )


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((16, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 16, 3, 3), (144, 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((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 64, 3, 3), (576, 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((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((256, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 256, 3, 3), (2304, 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, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((64, 32, 2, 2), (128, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((32, 16, 2, 2), (64, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((1, 16, 1, 1), (16, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((1, ), (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])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class Model(nn.Module):

    def __init__(self):
        super(Model, self).__init__()
        keep_rate = 0.5
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            3, stride=1, padding='same', bias=True)
        self.dropout1 = nn.Dropout2d(1 - keep_rate)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling1 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.dropout2 = nn.Dropout2d(1 - keep_rate)
        self.conv4 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling2 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.dropout3 = nn.Dropout2d(1 - keep_rate)
        self.conv6 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling3 = nn.MaxPool2d(2)
        self.conv7 = nn.Conv2d(in_channels=64, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout4 = nn.Dropout2d(1 - keep_rate)
        self.conv8 = nn.Conv2d(in_channels=128, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.maxpooling4 = nn.MaxPool2d(2)
        self.conv9 = nn.Conv2d(in_channels=128, out_channels=256,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout5 = nn.Dropout2d(1 - keep_rate)
        self.conv10 = nn.Conv2d(in_channels=256, out_channels=256,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv11 = nn.ConvTranspose2d(in_channels=256, out_channels=128,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv12 = nn.Conv2d(in_channels=256, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout6 = nn.Dropout2d(1 - keep_rate)
        self.conv13 = nn.Conv2d(in_channels=128, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv14 = nn.ConvTranspose2d(in_channels=128, out_channels=64,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv15 = nn.Conv2d(in_channels=128, out_channels=64,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout7 = nn.Dropout2d(1 - keep_rate)
        self.conv16 = nn.Conv2d(in_channels=64, out_channels=64,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv17 = nn.ConvTranspose2d(in_channels=64, out_channels=32,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv18 = nn.Conv2d(in_channels=64, out_channels=32,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout8 = nn.Dropout2d(1 - keep_rate)
        self.conv19 = nn.Conv2d(in_channels=32, out_channels=32,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv20 = nn.ConvTranspose2d(in_channels=32, out_channels=16,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv21 = nn.Conv2d(in_channels=32, out_channels=16,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout9 = nn.Dropout2d(1 - keep_rate)
        self.conv22 = nn.Conv2d(in_channels=16, out_channels=16,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.outputs = nn.Conv2d(in_channels=16, out_channels=1,
            kernel_size=1, stride=1, padding='same', bias=True)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.dropout1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x1 = self.maxpooling1(x)
        x1 = self.conv3(x1)
        x1 = F.relu(x1)
        x1 = self.dropout2(x1)
        x1 = self.conv4(x1)
        x1 = F.relu(x1)
        x2 = self.maxpooling2(x1)
        x2 = self.conv5(x2)
        x2 = F.relu(x2)
        x2 = self.dropout3(x2)
        x2 = self.conv6(x2)
        x2 = F.relu(x2)
        x3 = self.maxpooling3(x2)
        x3 = self.conv7(x3)
        x3 = F.relu(x3)
        x3 = self.dropout4(x3)
        x3 = self.conv8(x3)
        x3 = F.relu(x3)
        x4 = self.maxpooling4(x3)
        x4 = self.conv9(x4)
        x4 = F.relu(x4)
        x4 = self.dropout5(x4)
        x4 = self.conv10(x4)
        x4 = F.relu(x4)
        x5 = self.conv11(x4)
        x5 = torch.cat((x5, x3), 1)
        x5 = self.conv12(x5)
        x5 = F.relu(x5)
        x5 = self.dropout6(x5)
        x5 = self.conv13(x5)
        x5 = F.relu(x5)
        x6 = self.conv14(x5)
        x6 = torch.cat((x6, x2), 1)
        x6 = self.conv15(x6)
        x6 = F.relu(x6)
        x6 = self.dropout7(x6)
        x6 = self.conv16(x6)
        x6 = F.relu(x6)
        x7 = self.conv17(x6)
        x7 = torch.cat((x7, x1), 1)
        x7 = self.conv18(x7)
        x7 = F.relu(x7)
        x7 = self.dropout8(x7)
        x7 = self.conv19(x7)
        x7 = F.relu(x7)
        x8 = self.conv20(x7)
        x8 = torch.cat((x8, x), 1)
        x8 = self.conv21(x8)
        x8 = F.relu(x8)
        x8 = self.dropout9(x8)
        x8 = self.conv22(x8)
        x8 = F.relu(x8)
        outputs = self.outputs(x8)
        out = torch.sigmoid(outputs)
        return out


def get_inputs():
    return [torch.rand([4, 1, 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 % 16
    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_max_pool2d_with_indices_1(in_ptr0, 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)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, 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 // 1024 % 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_max_pool2d_with_indices_3(in_ptr0, 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)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_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 // 256 % 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_max_pool2d_with_indices_5(in_ptr0, 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)
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_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 // 64 % 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_max_pool2d_with_indices_7(in_ptr0, 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)
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, 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 // 16 % 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_cat_9(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 // 64 % 256
    x0 = xindex % 64
    x2 = xindex // 16384
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 8192 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 64 * (-128 + x1) + 8192 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_10(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 // 256 % 128
    x0 = xindex % 256
    x2 = xindex // 32768
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 256 * x1 + 16384 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 256 * (-64 + x1) + 16384 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_11(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 // 1024 % 64
    x0 = xindex % 1024
    x2 = xindex // 65536
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 32768 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 1024 * (-32 + x1) + 32768 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_12(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 % 32
    x0 = xindex % 4096
    x2 = xindex // 131072
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 32, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 4096 * (-16 + x1) + 65536 * x2), tmp10,
        other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_sigmoid_13(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, 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) = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 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, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (256,), (1,))
    assert_size_stride(primals_20, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_21, (256,), (1,))
    assert_size_stride(primals_22, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128, 256, 3, 3), (2304, 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, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_29, (64,), (1,))
    assert_size_stride(primals_30, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (64,), (1,))
    assert_size_stride(primals_32, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_33, (64,), (1,))
    assert_size_stride(primals_34, (64, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_35, (32,), (1,))
    assert_size_stride(primals_36, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_37, (32,), (1,))
    assert_size_stride(primals_38, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_39, (32,), (1,))
    assert_size_stride(primals_40, (32, 16, 2, 2), (64, 4, 2, 1))
    assert_size_stride(primals_41, (16,), (1,))
    assert_size_stride(primals_42, (16, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_43, (16,), (1,))
    assert_size_stride(primals_44, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_45, (16,), (1,))
    assert_size_stride(primals_46, (1, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_47, (1,), (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, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(262144)](buf1, primals_2, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(262144)](buf3, primals_5, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(65536)](buf3, buf4,
            buf5, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, 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, 32, 32), (32768, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_2[grid(131072)](buf7, primals_7, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_2[grid(131072)](buf9, primals_9, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(32768)](buf9, buf10,
            buf11, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf10, primals_10, 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, 16, 16), (16384, 256, 16, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_4[grid(65536)](buf13, primals_11,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_11
        buf14 = extern_kernels.convolution(buf13, primals_12, 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, 16, 16), (16384, 256, 16, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_4[grid(65536)](buf15, primals_13,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_13
        buf16 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.
            float32)
        buf17 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_5[grid(16384)](buf15,
            buf16, buf17, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        buf18 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_6[grid(32768)](buf19, primals_15,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_6[grid(32768)](buf21, primals_17,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf22 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        buf23 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.int8
            )
        triton_poi_fused_max_pool2d_with_indices_7[grid(8192)](buf21, buf22,
            buf23, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        buf24 = extern_kernels.convolution(buf22, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_8[grid(16384)](buf25, primals_19,
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 256, 4, 4), (4096, 16, 4, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_8[grid(16384)](buf27, primals_21,
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
        buf28 = extern_kernels.convolution(buf27, primals_22, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .float32)
        triton_poi_fused_cat_9[grid(65536)](buf28, primals_23, buf21, buf29,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del buf28
        del primals_23
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_relu_6[grid(32768)](buf31, primals_25,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 128, 8, 8), (8192, 64, 8, 1))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_relu_6[grid(32768)](buf33, primals_27,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_27
        buf34 = extern_kernels.convolution(buf33, primals_28, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_cat_10[grid(131072)](buf34, primals_29, buf15,
            buf35, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf34
        del primals_29
        buf36 = extern_kernels.convolution(buf35, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_4[grid(65536)](buf37, primals_31,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_31
        buf38 = extern_kernels.convolution(buf37, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_relu_4[grid(65536)](buf39, primals_33,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_33
        buf40 = extern_kernels.convolution(buf39, primals_34, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf41 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_cat_11[grid(262144)](buf40, primals_35, buf9,
            buf41, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf40
        del primals_35
        buf42 = extern_kernels.convolution(buf41, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf43 = buf42
        del buf42
        triton_poi_fused_convolution_relu_2[grid(131072)](buf43, primals_37,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_37
        buf44 = extern_kernels.convolution(buf43, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf45 = buf44
        del buf44
        triton_poi_fused_convolution_relu_2[grid(131072)](buf45, primals_39,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_39
        buf46 = extern_kernels.convolution(buf45, primals_40, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf47 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_12[grid(524288)](buf46, primals_41, buf3,
            buf47, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del buf46
        del primals_41
        buf48 = extern_kernels.convolution(buf47, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf49 = buf48
        del buf48
        triton_poi_fused_convolution_relu_0[grid(262144)](buf49, primals_43,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_43
        buf50 = extern_kernels.convolution(buf49, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf51 = buf50
        del buf50
        triton_poi_fused_convolution_relu_0[grid(262144)](buf51, primals_45,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_45
        buf52 = extern_kernels.convolution(buf51, primals_46, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf53 = buf52
        del buf52
        triton_poi_fused_convolution_sigmoid_13[grid(16384)](buf53,
            primals_47, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_47
    return (buf53, 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, buf1, buf3, buf4,
        buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17, buf19,
        buf21, buf22, buf23, buf25, buf27, buf29, buf31, buf33, buf35,
        buf37, buf39, buf41, buf43, buf45, buf47, buf49, buf51, buf53)


class ModelNew(nn.Module):

    def __init__(self):
        super(ModelNew, self).__init__()
        keep_rate = 0.5
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            3, stride=1, padding='same', bias=True)
        self.dropout1 = nn.Dropout2d(1 - keep_rate)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling1 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.dropout2 = nn.Dropout2d(1 - keep_rate)
        self.conv4 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling2 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.dropout3 = nn.Dropout2d(1 - keep_rate)
        self.conv6 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding='same', bias=True)
        self.maxpooling3 = nn.MaxPool2d(2)
        self.conv7 = nn.Conv2d(in_channels=64, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout4 = nn.Dropout2d(1 - keep_rate)
        self.conv8 = nn.Conv2d(in_channels=128, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.maxpooling4 = nn.MaxPool2d(2)
        self.conv9 = nn.Conv2d(in_channels=128, out_channels=256,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout5 = nn.Dropout2d(1 - keep_rate)
        self.conv10 = nn.Conv2d(in_channels=256, out_channels=256,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv11 = nn.ConvTranspose2d(in_channels=256, out_channels=128,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv12 = nn.Conv2d(in_channels=256, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout6 = nn.Dropout2d(1 - keep_rate)
        self.conv13 = nn.Conv2d(in_channels=128, out_channels=128,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv14 = nn.ConvTranspose2d(in_channels=128, out_channels=64,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv15 = nn.Conv2d(in_channels=128, out_channels=64,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout7 = nn.Dropout2d(1 - keep_rate)
        self.conv16 = nn.Conv2d(in_channels=64, out_channels=64,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv17 = nn.ConvTranspose2d(in_channels=64, out_channels=32,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv18 = nn.Conv2d(in_channels=64, out_channels=32,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout8 = nn.Dropout2d(1 - keep_rate)
        self.conv19 = nn.Conv2d(in_channels=32, out_channels=32,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.conv20 = nn.ConvTranspose2d(in_channels=32, out_channels=16,
            kernel_size=2, stride=2, padding=0, bias=True)
        self.conv21 = nn.Conv2d(in_channels=32, out_channels=16,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.dropout9 = nn.Dropout2d(1 - keep_rate)
        self.conv22 = nn.Conv2d(in_channels=16, out_channels=16,
            kernel_size=3, stride=1, padding='same', bias=True)
        self.outputs = nn.Conv2d(in_channels=16, out_channels=1,
            kernel_size=1, stride=1, padding='same', bias=True)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_12 = self.conv6.weight
        primals_13 = self.conv6.bias
        primals_14 = self.conv7.weight
        primals_15 = self.conv7.bias
        primals_16 = self.conv8.weight
        primals_17 = self.conv8.bias
        primals_18 = self.conv9.weight
        primals_19 = self.conv9.bias
        primals_20 = self.conv10.weight
        primals_21 = self.conv10.bias
        primals_22 = self.conv11.weight
        primals_23 = self.conv11.bias
        primals_24 = self.conv12.weight
        primals_25 = self.conv12.bias
        primals_26 = self.conv13.weight
        primals_27 = self.conv13.bias
        primals_28 = self.conv14.weight
        primals_29 = self.conv14.bias
        primals_30 = self.conv15.weight
        primals_31 = self.conv15.bias
        primals_32 = self.conv16.weight
        primals_33 = self.conv16.bias
        primals_34 = self.conv17.weight
        primals_35 = self.conv17.bias
        primals_36 = self.conv18.weight
        primals_37 = self.conv18.bias
        primals_38 = self.conv19.weight
        primals_39 = self.conv19.bias
        primals_40 = self.conv20.weight
        primals_41 = self.conv20.bias
        primals_42 = self.conv21.weight
        primals_43 = self.conv21.bias
        primals_44 = self.conv22.weight
        primals_45 = self.conv22.bias
        primals_46 = self.outputs.weight
        primals_47 = self.outputs.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])
        return output[0]
