# 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/gw/cgwvngevezyioi33llipcuwltuqtivozkg4knqbabc42x3aygqlb.py
# Topologically Sorted Source Nodes: [conv2d, conv1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv1 => gt, mul, where
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=3] = 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 = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/36/c36goqekbheqmzqx63ibehvw5xzi6nve5f33bertb3dmpfgep4fh.py
# Topologically Sorted Source Nodes: [max1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max1 => 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=[131072], 
    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 = 131072
    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/wi/cwilhwmrmn5re6wis6cj3sphepuvsbozh5l3iow7ctwufykeb3jf.py
# Topologically Sorted Source Nodes: [conv2d_2, conv2], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2 => gt_2, mul_2, where_2
#   conv2d_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = 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 = {})
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused_convolution_leaky_relu_2 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 64
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gq/cgqvpghyzwkbypvbvlyig6ohuplw6qvhn73hkwj6auj5e2m5mqio.py
# Topologically Sorted Source Nodes: [max2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max2 => 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=[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_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 = 65536
    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/nt/cntd42ytyx73ifo3kbz7syyzhetuaz5cp4rjzlg37ux3mf2ubiwv.py
# Topologically Sorted Source Nodes: [conv2d_4, conv3], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   conv3 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=3] = 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 = {})
#   %gt_4 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_4, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused_convolution_leaky_relu_4 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 // 256) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sa/csaftzx2wizpjlh3kfhvehexsbodntymkhlieviiunpbabb3qdpw.py
# Topologically Sorted Source Nodes: [max3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max3 => 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=[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_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 = 32768
    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/u3/cu3zfyezkhneeddkwvkcoag2f7cscbzxoi6clw753qu7ijag5g25.py
# Topologically Sorted Source Nodes: [conv2d_6, conv4], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   conv4 => gt_6, mul_6, where_6
# Graph fragment:
#   %convolution_6 : [num_users=3] = 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 = {})
#   %gt_6 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_6, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_6, 0.2), kwargs = {})
#   %where_6 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %convolution_6, %mul_6), kwargs = {})
triton_poi_fused_convolution_leaky_relu_6 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_6(in_ptr0, in_ptr1, 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)
    x3 = xindex
    x1 = (xindex // 64) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v4/cv4bgr5ihpscky5gololr6sh7rfrpysfa3a6blhdfz537nybxcot.py
# Topologically Sorted Source Nodes: [max4], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max4 => 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=[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_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 = 16384
    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/ee/cee2vggtilskpwvzwna6ytg7q44zddiwmayfxndtiiqaibss6s7q.py
# Topologically Sorted Source Nodes: [conv2d_8, conv5], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_8 => convolution_8
#   conv5 => gt_8, mul_8, where_8
# Graph fragment:
#   %convolution_8 : [num_users=3] = 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 = {})
#   %gt_8 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_8, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_8, 0.2), kwargs = {})
#   %where_8 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_8, %convolution_8, %mul_8), kwargs = {})
triton_poi_fused_convolution_leaky_relu_8 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_8(in_ptr0, in_ptr1, 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)
    x3 = xindex
    x1 = (xindex // 16) % 512
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/eq/ceqqib3urhabgkow4sc3z3e7vhjsujpz7te5cmejrbqsvm5oh7dw.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_7, %convolution_10], 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=[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_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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 512
    x0 = xindex % 64
    x2 = (xindex // 32768)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x1) + (16384*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 512, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (64*((-256) + x1)) + (16384*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-256) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/um/cum4xmhzut6oemgsz6mp7azjblmpjz34bv7kc6f2ybmylngpgs7x.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_5, %convolution_13], 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=[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_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 = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 256) % 256
    x0 = xindex % 256
    x2 = (xindex // 65536)
    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 + (256*x1) + (32768*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (256*((-128) + x1)) + (32768*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-128) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q6/cq67vzkba6k77wfo6afbzsxslwbeuroobz57p6dd2te6xiuxqail.py
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_3, %convolution_16], 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=[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_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 = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 1024) % 128
    x0 = xindex % 1024
    x2 = (xindex // 131072)
    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 + (1024*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (1024*((-64) + x1)) + (65536*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-64) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/aw/cawgdzi2uiagnfffsouhjq3jst4gtkumr334kwq47jogcmzyhfss.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_1, %convolution_19], 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=[1048576], 
    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 = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 64
    x0 = xindex % 4096
    x2 = (xindex // 262144)
    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 + (4096*x1) + (131072*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 64, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-32) + x1)) + (131072*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-32) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wn/cwn3n6pc3p5yfpg2qoyin6heoasg375jez7odcopiodaao5bowrc.py
# Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.pixel_shuffle]
# Source node to ATen node mapping:
#   out_4 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_pixel_shuffle_13 = async_compile.triton('triton_poi_fused_pixel_shuffle_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=[131072, 2], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_pixel_shuffle_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_pixel_shuffle_13(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 98304
    xnumel = 2
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x5 = xindex
    y0 = yindex % 64
    y1 = (yindex // 64) % 2
    y2 = (yindex // 128) % 64
    y6 = (yindex // 8192)
    y3 = (yindex // 8192) % 3
    y7 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (64*y2) + (4096*x5) + (8192*y1) + (16384*y6)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + (2*y1) + (4*y3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x5 + (2*y7)), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47 = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (128, ), (1, ))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128, ), (1, ))
    assert_size_stride(primals_14, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (256, ), (1, ))
    assert_size_stride(primals_18, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_19, (512, ), (1, ))
    assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_21, (512, ), (1, ))
    assert_size_stride(primals_22, (512, 256, 2, 2), (1024, 4, 2, 1))
    assert_size_stride(primals_23, (256, ), (1, ))
    assert_size_stride(primals_24, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (256, ), (1, ))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256, ), (1, ))
    assert_size_stride(primals_28, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_29, (128, ), (1, ))
    assert_size_stride(primals_30, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_31, (128, ), (1, ))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128, ), (1, ))
    assert_size_stride(primals_34, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_35, (64, ), (1, ))
    assert_size_stride(primals_36, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_37, (64, ), (1, ))
    assert_size_stride(primals_38, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_39, (64, ), (1, ))
    assert_size_stride(primals_40, (64, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_41, (32, ), (1, ))
    assert_size_stride(primals_42, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_43, (32, ), (1, ))
    assert_size_stride(primals_44, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_45, (32, ), (1, ))
    assert_size_stride(primals_46, (12, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (12, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, conv1], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 524288, grid=grid(524288), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf5 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, conv1_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf3, primals_5, buf4, buf5, 524288, grid=grid(524288), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf5, buf6, buf7, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, conv2], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf8, primals_7, buf9, buf10, 262144, grid=grid(262144), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf13 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, conv2_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf11, primals_9, buf12, buf13, 262144, grid=grid(262144), stream=stream0)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf13, buf14, buf15, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf14, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf17 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_4, conv3], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf16, primals_11, buf17, buf18, 131072, grid=grid(131072), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf20 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf21 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, conv3_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf19, primals_13, buf20, buf21, 131072, grid=grid(131072), stream=stream0)
        del primals_13
        buf22 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf21, buf22, buf23, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf22, primals_14, 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, 8, 8), (16384, 64, 8, 1))
        buf25 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_6, conv4], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf24, primals_15, buf25, buf26, 65536, grid=grid(65536), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf26, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf29 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, conv4_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf27, primals_17, buf28, buf29, 65536, grid=grid(65536), stream=stream0)
        del buf27
        del primals_17
        buf30 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        buf31 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max4], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf29, buf30, buf31, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf30, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf33 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        buf34 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_8, conv5], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf32, primals_19, buf33, buf34, 32768, grid=grid(32768), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf35 = extern_kernels.convolution(buf34, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf36 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        buf37 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, conv5_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf35, primals_21, buf36, buf37, 32768, grid=grid(32768), stream=stream0)
        del buf35
        del primals_21
        # Topologically Sorted Source Nodes: [upconv], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_22, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf39 = reinterpret_tensor(buf19, (4, 512, 8, 8), (32768, 64, 8, 1), 0); del buf19  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
        triton_poi_fused_cat_9.run(buf29, buf38, primals_23, buf39, 131072, grid=grid(131072), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf41 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf42 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10, conv6], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf40, primals_25, buf41, buf42, 65536, grid=grid(65536), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf44 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf45 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, conv6_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf43, primals_27, buf44, buf45, 65536, grid=grid(65536), stream=stream0)
        del buf43
        del primals_27
        # Topologically Sorted Source Nodes: [upconv_1], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_28, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf47 = reinterpret_tensor(buf11, (4, 256, 16, 16), (65536, 256, 16, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_10.run(buf21, buf46, primals_29, buf47, 262144, grid=grid(262144), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf49 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf50 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12, conv7], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf48, primals_31, buf49, buf50, 131072, grid=grid(131072), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf52 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf53 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, conv7_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf51, primals_33, buf52, buf53, 131072, grid=grid(131072), stream=stream0)
        del buf51
        del primals_33
        # Topologically Sorted Source Nodes: [upconv_2], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_34, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf55 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 32, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_11.run(buf13, buf54, primals_35, buf55, 524288, grid=grid(524288), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf57 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf58 = buf54; del buf54  # reuse
        # Topologically Sorted Source Nodes: [conv2d_14, conv8], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf56, primals_37, buf57, buf58, 262144, grid=grid(262144), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf59 = extern_kernels.convolution(buf58, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf60 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf61 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [conv2d_15, conv8_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf59, primals_39, buf60, buf61, 262144, grid=grid(262144), stream=stream0)
        del buf59
        del primals_39
        # Topologically Sorted Source Nodes: [upconv_3], Original ATen: [aten.convolution]
        buf62 = extern_kernels.convolution(buf61, primals_40, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf63 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_12.run(buf5, buf62, primals_41, buf63, 1048576, grid=grid(1048576), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf63, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf65 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf66 = buf62; del buf62  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16, conv9], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf64, primals_43, buf65, buf66, 524288, grid=grid(524288), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf67 = extern_kernels.convolution(buf66, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf68 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf69 = buf64; del buf64  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17, conv9_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf67, primals_45, buf68, buf69, 524288, grid=grid(524288), stream=stream0)
        del buf67
        del primals_45
        # Topologically Sorted Source Nodes: [conv10], Original ATen: [aten.convolution]
        buf70 = extern_kernels.convolution(buf69, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 12, 64, 64), (49152, 4096, 64, 1))
        buf71 = empty_strided_cuda((4, 3, 64, 2, 64, 2), (49152, 16384, 256, 128, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.pixel_shuffle]
        triton_poi_fused_pixel_shuffle_13.run(buf70, primals_47, buf71, 98304, 2, grid=grid(98304, 2), stream=stream0)
        del buf70
        del primals_47
    return (reinterpret_tensor(buf71, (4, 3, 128, 128), (49152, 16384, 128, 1), 0), 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, buf2, buf4, buf5, buf6, buf7, buf9, buf10, buf12, buf13, buf14, buf15, buf17, buf18, buf20, buf21, buf22, buf23, buf25, buf26, buf28, buf29, buf30, buf31, buf33, buf34, buf36, buf37, buf39, buf41, buf42, buf44, buf45, buf47, buf49, buf50, buf52, buf53, buf55, buf57, buf58, buf60, buf61, buf63, buf65, buf66, buf68, buf69, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((512, 256, 2, 2), (1024, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((256, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((128, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((64, 32, 2, 2), (128, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((12, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((12, ), (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 UNet(nn.Module):

    def __init__(self):
        super().__init__()
        self.lrelu = nn.LeakyReLU(0.2)
        self.maxpool = nn.MaxPool2d(2)
        self.conv1_0 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv1_1 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv2_0 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv2_1 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv3_0 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3_1 = nn.Conv2d(128, 128, 3, padding=1)
        self.conv4_0 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4_1 = nn.Conv2d(256, 256, 3, padding=1)
        self.conv5_0 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
        self.upconv6_0 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv6_1 = nn.Conv2d(512, 256, 3, padding=1)
        self.conv6_2 = nn.Conv2d(256, 256, 3, padding=1)
        self.upconv7_0 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv7_1 = nn.Conv2d(256, 128, 3, padding=1)
        self.conv7_2 = nn.Conv2d(128, 128, 3, padding=1)
        self.upconv8_0 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv8_1 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv8_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.upconv9_0 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
        self.conv9_1 = nn.Conv2d(64, 32, 3, padding=1)
        self.conv9_2 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv10 = nn.Conv2d(32, 12, 3, padding=1)

    def upsample_and_concat(self, x1, x2, upconv_func):
        upconv = upconv_func(x1)
        out = torch.cat([x2, upconv], dim=1)
        return out

    def forward(self, x):
        conv1 = self.lrelu(self.conv1_0(x))
        conv1 = self.lrelu(self.conv1_1(conv1))
        max1 = nn.MaxPool2d(2)(conv1)
        conv2 = self.lrelu(self.conv2_0(max1))
        conv2 = self.lrelu(self.conv2_1(conv2))
        max2 = nn.MaxPool2d(2)(conv2)
        conv3 = self.lrelu(self.conv3_0(max2))
        conv3 = self.lrelu(self.conv3_1(conv3))
        max3 = nn.MaxPool2d(2)(conv3)
        conv4 = self.lrelu(self.conv4_0(max3))
        conv4 = self.lrelu(self.conv4_1(conv4))
        max4 = nn.MaxPool2d(2)(conv4)
        conv5 = self.lrelu(self.conv5_0(max4))
        conv5 = self.lrelu(self.conv5_1(conv5))
        upconv6 = self.upsample_and_concat(conv5, conv4, self.upconv6_0)
        conv6 = self.lrelu(self.conv6_1(upconv6))
        conv6 = self.lrelu(self.conv6_2(conv6))
        upconv7 = self.upsample_and_concat(conv6, conv3, self.upconv7_0)
        conv7 = self.lrelu(self.conv7_1(upconv7))
        conv7 = self.lrelu(self.conv7_2(conv7))
        upconv8 = self.upsample_and_concat(conv7, conv2, self.upconv8_0)
        conv8 = self.lrelu(self.conv8_1(upconv8))
        conv8 = self.lrelu(self.conv8_2(conv8))
        upconv9 = self.upsample_and_concat(conv8, conv1, self.upconv9_0)
        conv9 = self.lrelu(self.conv9_1(upconv9))
        conv9 = self.lrelu(self.conv9_2(conv9))
        conv10 = self.conv10(conv9)
        out = F.pixel_shuffle(conv10, 2)
        return out


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


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

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


@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, 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_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, 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_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, 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_leaky_relu_6(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, 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_leaky_relu_8(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 512
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, 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 % 512
    x0 = xindex % 64
    x2 = xindex // 32768
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 16384 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * (-256 + x1) + 16384 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-256 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, 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 % 256
    x0 = xindex % 256
    x2 = xindex // 65536
    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 + 256 * x1 + 32768 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 256 * (-128 + x1) + 32768 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-128 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, 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 % 128
    x0 = xindex % 1024
    x2 = xindex // 131072
    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 + 1024 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 1024 * (-64 + x1) + 65536 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-64 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, 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 % 64
    x0 = xindex % 4096
    x2 = xindex // 262144
    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 + 4096 * x1 + 131072 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-32 + x1) + 131072 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-32 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_pixel_shuffle_13(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 2
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x5 = xindex
    y0 = yindex % 64
    y1 = yindex // 64 % 2
    y2 = yindex // 128 % 64
    y6 = yindex // 8192
    y3 = yindex // 8192 % 3
    y7 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * y2 + 4096 * x5 + 8192 * y1 + 16384 *
        y6), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 2 * y1 + 4 * y3), xmask, eviction_policy
        ='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x5 + 2 * y7), tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47) = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (256,), (1,))
    assert_size_stride(primals_18, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_19, (512,), (1,))
    assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_21, (512,), (1,))
    assert_size_stride(primals_22, (512, 256, 2, 2), (1024, 4, 2, 1))
    assert_size_stride(primals_23, (256,), (1,))
    assert_size_stride(primals_24, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (256,), (1,))
    assert_size_stride(primals_26, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_27, (256,), (1,))
    assert_size_stride(primals_28, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_29, (128,), (1,))
    assert_size_stride(primals_30, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_31, (128,), (1,))
    assert_size_stride(primals_32, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_35, (64,), (1,))
    assert_size_stride(primals_36, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_37, (64,), (1,))
    assert_size_stride(primals_38, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_39, (64,), (1,))
    assert_size_stride(primals_40, (64, 32, 2, 2), (128, 4, 2, 1))
    assert_size_stride(primals_41, (32,), (1,))
    assert_size_stride(primals_42, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_43, (32,), (1,))
    assert_size_stride(primals_44, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_45, (32,), (1,))
    assert_size_stride(primals_46, (12, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (12,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf0,
            primals_2, buf1, buf2, 524288, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf5 = buf0
        del buf0
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf3,
            primals_5, buf4, buf5, 524288, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        buf7 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(131072)](buf5, buf6,
            buf7, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        buf8 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf10 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf8,
            primals_7, buf9, buf10, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_7
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf13 = buf8
        del buf8
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf11,
            primals_9, buf12, buf13, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        buf15 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(65536)](buf13,
            buf14, buf15, 65536, XBLOCK=256, num_warps=4, num_stages=1)
        buf16 = extern_kernels.convolution(buf14, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf17 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf16,
            primals_11, buf17, buf18, 131072, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_11
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf20 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf21 = buf16
        del buf16
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf19,
            primals_13, buf20, buf21, 131072, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_13
        buf22 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        buf23 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.int8
            )
        triton_poi_fused_max_pool2d_with_indices_5[grid(32768)](buf21,
            buf22, buf23, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        buf24 = extern_kernels.convolution(buf22, primals_14, 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, 8, 8), (16384, 64, 8, 1))
        buf25 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf24,
            primals_15, buf25, buf26, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_15
        buf27 = extern_kernels.convolution(buf26, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf29 = buf24
        del buf24
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf27,
            primals_17, buf28, buf29, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf27
        del primals_17
        buf30 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        buf31 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.int8
            )
        triton_poi_fused_max_pool2d_with_indices_7[grid(16384)](buf29,
            buf30, buf31, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        buf32 = extern_kernels.convolution(buf30, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf33 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        buf34 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf32,
            primals_19, buf33, buf34, 32768, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_19
        buf35 = extern_kernels.convolution(buf34, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf36 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        buf37 = buf32
        del buf32
        triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf35,
            primals_21, buf36, buf37, 32768, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf35
        del primals_21
        buf38 = extern_kernels.convolution(buf37, primals_22, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf39 = reinterpret_tensor(buf19, (4, 512, 8, 8), (32768, 64, 8, 1), 0)
        del buf19
        triton_poi_fused_cat_9[grid(131072)](buf29, buf38, primals_23,
            buf39, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_23
        buf40 = extern_kernels.convolution(buf39, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf41 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf42 = buf38
        del buf38
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf40,
            primals_25, buf41, buf42, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del primals_25
        buf43 = extern_kernels.convolution(buf42, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf44 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf45 = buf40
        del buf40
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf43,
            primals_27, buf44, buf45, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf43
        del primals_27
        buf46 = extern_kernels.convolution(buf45, primals_28, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf47 = reinterpret_tensor(buf11, (4, 256, 16, 16), (65536, 256, 16,
            1), 0)
        del buf11
        triton_poi_fused_cat_10[grid(262144)](buf21, buf46, primals_29,
            buf47, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_29
        buf48 = extern_kernels.convolution(buf47, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf49 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf50 = buf46
        del buf46
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf48,
            primals_31, buf49, buf50, 131072, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_31
        buf51 = extern_kernels.convolution(buf50, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf52 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf53 = buf48
        del buf48
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf51,
            primals_33, buf52, buf53, 131072, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf51
        del primals_33
        buf54 = extern_kernels.convolution(buf53, primals_34, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf55 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 
            32, 1), 0)
        del buf3
        triton_poi_fused_cat_11[grid(524288)](buf13, buf54, primals_35,
            buf55, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_35
        buf56 = extern_kernels.convolution(buf55, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf57 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf58 = buf54
        del buf54
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf56,
            primals_37, buf57, buf58, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_37
        buf59 = extern_kernels.convolution(buf58, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf60 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf61 = buf56
        del buf56
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf59,
            primals_39, buf60, buf61, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf59
        del primals_39
        buf62 = extern_kernels.convolution(buf61, primals_40, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf63 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_12[grid(1048576)](buf5, buf62, primals_41,
            buf63, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_41
        buf64 = extern_kernels.convolution(buf63, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf65 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf66 = buf62
        del buf62
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf64,
            primals_43, buf65, buf66, 524288, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_43
        buf67 = extern_kernels.convolution(buf66, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf68 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf69 = buf64
        del buf64
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf67,
            primals_45, buf68, buf69, 524288, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf67
        del primals_45
        buf70 = extern_kernels.convolution(buf69, primals_46, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 12, 64, 64), (49152, 4096, 64, 1))
        buf71 = empty_strided_cuda((4, 3, 64, 2, 64, 2), (49152, 16384, 256,
            128, 2, 1), torch.float32)
        triton_poi_fused_pixel_shuffle_13[grid(98304, 2)](buf70, primals_47,
            buf71, 98304, 2, XBLOCK=2, YBLOCK=512, num_warps=4, num_stages=1)
        del buf70
        del primals_47
    return (reinterpret_tensor(buf71, (4, 3, 128, 128), (49152, 16384, 128,
        1), 0), 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, buf2, buf4,
        buf5, buf6, buf7, buf9, buf10, buf12, buf13, buf14, buf15, buf17,
        buf18, buf20, buf21, buf22, buf23, buf25, buf26, buf28, buf29,
        buf30, buf31, buf33, buf34, buf36, buf37, buf39, buf41, buf42,
        buf44, buf45, buf47, buf49, buf50, buf52, buf53, buf55, buf57,
        buf58, buf60, buf61, buf63, buf65, buf66, buf68, buf69)


class UNetNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.lrelu = nn.LeakyReLU(0.2)
        self.maxpool = nn.MaxPool2d(2)
        self.conv1_0 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv1_1 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv2_0 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv2_1 = nn.Conv2d(64, 64, 3, padding=1)
        self.conv3_0 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3_1 = nn.Conv2d(128, 128, 3, padding=1)
        self.conv4_0 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4_1 = nn.Conv2d(256, 256, 3, padding=1)
        self.conv5_0 = nn.Conv2d(256, 512, 3, padding=1)
        self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
        self.upconv6_0 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv6_1 = nn.Conv2d(512, 256, 3, padding=1)
        self.conv6_2 = nn.Conv2d(256, 256, 3, padding=1)
        self.upconv7_0 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv7_1 = nn.Conv2d(256, 128, 3, padding=1)
        self.conv7_2 = nn.Conv2d(128, 128, 3, padding=1)
        self.upconv8_0 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv8_1 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv8_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.upconv9_0 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
        self.conv9_1 = nn.Conv2d(64, 32, 3, padding=1)
        self.conv9_2 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv10 = nn.Conv2d(32, 12, 3, padding=1)

    def upsample_and_concat(self, x1, x2, upconv_func):
        upconv = upconv_func(x1)
        out = torch.cat([x2, upconv], dim=1)
        return out

    def forward(self, input_0):
        primals_1 = self.conv1_0.weight
        primals_2 = self.conv1_0.bias
        primals_4 = self.conv1_1.weight
        primals_5 = self.conv1_1.bias
        primals_6 = self.conv2_0.weight
        primals_7 = self.conv2_0.bias
        primals_8 = self.conv2_1.weight
        primals_9 = self.conv2_1.bias
        primals_10 = self.conv3_0.weight
        primals_11 = self.conv3_0.bias
        primals_12 = self.conv3_1.weight
        primals_13 = self.conv3_1.bias
        primals_14 = self.conv4_0.weight
        primals_15 = self.conv4_0.bias
        primals_16 = self.conv4_1.weight
        primals_17 = self.conv4_1.bias
        primals_18 = self.conv5_0.weight
        primals_19 = self.conv5_0.bias
        primals_20 = self.conv5_1.weight
        primals_21 = self.conv5_1.bias
        primals_22 = self.upconv6_0.weight
        primals_23 = self.upconv6_0.bias
        primals_24 = self.conv6_1.weight
        primals_25 = self.conv6_1.bias
        primals_26 = self.conv6_2.weight
        primals_27 = self.conv6_2.bias
        primals_28 = self.upconv7_0.weight
        primals_29 = self.upconv7_0.bias
        primals_30 = self.conv7_1.weight
        primals_31 = self.conv7_1.bias
        primals_32 = self.conv7_2.weight
        primals_33 = self.conv7_2.bias
        primals_34 = self.upconv8_0.weight
        primals_35 = self.upconv8_0.bias
        primals_36 = self.conv8_1.weight
        primals_37 = self.conv8_1.bias
        primals_38 = self.conv8_2.weight
        primals_39 = self.conv8_2.bias
        primals_40 = self.upconv9_0.weight
        primals_41 = self.upconv9_0.bias
        primals_42 = self.conv9_1.weight
        primals_43 = self.conv9_1.bias
        primals_44 = self.conv9_2.weight
        primals_45 = self.conv9_2.bias
        primals_46 = self.conv10.weight
        primals_47 = self.conv10.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]
