# 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_8/inductor_cache/jo/cjolh7wy3losq75bea7heuxra52smjn2phczl4xzt2smarbxy3nj.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [3, 3], [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, 0.1), 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 = 0.1
    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_8/inductor_cache/o4/co4xz3bhphdn2kq3lke3433wpdtqt6r3irqbdr7hp46ou2slvxop.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_1 => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_1, [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_1 = async_compile.triton('triton_poi_fused_avg_pool2d_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_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_avg_pool2d_1(in_ptr0, 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)
    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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/b2/cb2heynzbbb2idhib26qs23x62rr3vu36ahp3tksyhjfahippc67.py
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_2 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d, %primals_6, %primals_7, [1, 1], [2, 2], [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, 0.1), 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 = 0.1
    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_8/inductor_cache/xj/cxjbrfzbed7bo2iy4m5zuii5z5cssze6tfcgrk2jehpphz5b77jh.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_4 => avg_pool2d_1
# Graph fragment:
#   %avg_pool2d_1 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_3, [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_3 = async_compile.triton('triton_poi_fused_avg_pool2d_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_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_avg_pool2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/le/cleento7jh4h7b7b25wgw4ax6qfmthojxlfqfgkaohjqgn6pqwco.py
# Topologically Sorted Source Nodes: [conv2d_4, x_5], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   x_5 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_1, %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, 0.1), 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 = 0.1
    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_8/inductor_cache/s5/cs5zukgmdewmnpcrozw2m273bpclzrkypvc2xaub2gmoc5saabvv.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_7 => avg_pool2d_2
# Graph fragment:
#   %avg_pool2d_2 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_5, [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_5 = async_compile.triton('triton_poi_fused_avg_pool2d_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_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_avg_pool2d_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4u/c4urfqsk2wuyrkcnwy7b2uiwmecrugesubdiuadavwqtcisyhwz4.py
# Topologically Sorted Source Nodes: [conv2d_6, x_8], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   x_8 => gt_6, mul_6, where_6
# Graph fragment:
#   %convolution_6 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_2, %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, 0.1), 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 = 0.1
    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_8/inductor_cache/3z/c3zje6b5ccaz3n4winpmxo6y4niaoldocb7ilvkg5sorj2nqvjfa.py
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_10 => avg_pool2d_3
# Graph fragment:
#   %avg_pool2d_3 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_7, [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_7 = async_compile.triton('triton_poi_fused_avg_pool2d_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_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_avg_pool2d_7(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/oy/coyjgpdjbipe737iasihk5ensjtmspgnzblyyy7mrlypqho5vuyg.py
# Topologically Sorted Source Nodes: [conv2d_8, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_8 => convolution_8
#   x_11 => gt_8, mul_8, where_8
# Graph fragment:
#   %convolution_8 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_3, %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, 0.1), 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 = 0.1
    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_8/inductor_cache/j6/cj6wkwoaluxhwqnux44qht6o5xye6n3bfqi54esxnpytd6m2qyjn.py
# Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_13 => avg_pool2d_4
# Graph fragment:
#   %avg_pool2d_4 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_9, [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_9 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_9', '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_avg_pool2d_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (8*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x1)), None, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/i6/ci6sgepehwucwp2knnf7ujr55xjh7bis2i3kdyon6flrsjhhdhyi.py
# Topologically Sorted Source Nodes: [conv2d_10, x_14], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_10
#   x_14 => gt_10, mul_10, where_10
# Graph fragment:
#   %convolution_10 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_4, %primals_22, %primals_23, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_10 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_10, 0), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_10, 0.1), kwargs = {})
#   %where_10 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_10, %convolution_10, %mul_10), kwargs = {})
triton_poi_fused_convolution_leaky_relu_10 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_10', '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_10(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4) % 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 = 0.1
    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_8/inductor_cache/ep/cepxp5elnxw6qvzcibzdejr6ov2i7hn664ixt6w4vzlrorsdstiq.py
# Topologically Sorted Source Nodes: [conv2d_11, x_15], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   x_15 => gt_11
# Graph fragment:
#   %convolution_11 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_10, %primals_24, %primals_25, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_11 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_11, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_11 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4) % 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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/r4/cr4hpkpezpllmtrycjdjyfyalsg3igxkpp5ddup6ueansg3uhioj.py
# Topologically Sorted Source Nodes: [x_16], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_16 => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
triton_poi_fused__to_copy_12 = async_compile.triton('triton_poi_fused__to_copy_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_12(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/it/citeiab2byvsltguyuzd2s2joq6e6z355s7h7bam6hgio5s5cret.py
# Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_16 => add_1, clamp_max
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_1, 1), kwargs = {})
#   %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_1, 1), kwargs = {})
triton_poi_fused_add_clamp_13 = async_compile.triton('triton_poi_fused_add_clamp_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_13(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = triton_helpers.minimum(tmp10, tmp9)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tq/ctqegi24pxetbae63246pykqjalfftx6xr5vt4fhudr7ehpmpbyv.py
# Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   x_16 => add, clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul_12, sub, sub_2
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.5), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_12, 0.5), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_14(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ar/carrwnezxoitom5qyzrwvxxe2xsarer32dfybfdk3w4gttj5i277.py
# Topologically Sorted Source Nodes: [conv2d_11, x_15, x_16], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   x_15 => mul_11, where_11
#   x_16 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_4, add_5, add_6, mul_14, mul_15, mul_16, sub_3, sub_4, sub_6
# Graph fragment:
#   %convolution_11 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_10, %primals_24, %primals_25, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_11, 0.1), kwargs = {})
#   %where_11 : [num_users=4] = call_function[target=torch.ops.aten.where.self](args = (%gt_11, %convolution_11, %mul_11), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_11, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_11, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_11, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_11, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %clamp_max_2), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_14), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_15), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_3), kwargs = {})
#   %add_6 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_16), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*i1', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_leaky_relu_mul_sub_15(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x6 = (xindex // 16)
    x2 = (xindex // 16) % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 2, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + (2*tmp19) + (4*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + (2*tmp19) + (4*x6)), None, eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + (2*tmp19) + (4*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + (2*tmp19) + (4*x6)), None, eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + (2*tmp4) + (4*x6)), None, eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + (x4), tmp49, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vm/cvmoqavquuan3erpml2tllmmlw2pfct5mokbplbbjboljuyvw7db.py
# Topologically Sorted Source Nodes: [conv2d_12, x_17], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   x_17 => gt_12
# Graph fragment:
#   %convolution_12 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_6, %primals_26, %primals_27, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_12 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_12, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_16 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_16', '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_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/zq/czqy62awvhrtl5r6fvvk4ufd5wffutbs7uz3a6rvpxyaj5tosmne.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_12, %where_9], 1), kwargs = {})
triton_poi_fused_cat_17 = async_compile.triton('triton_poi_fused_cat_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_17', '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_cat_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 1024
    x0 = xindex % 16
    x2 = (xindex // 16384)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (8192*x2)), tmp4, other=0.0).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + (16*x1) + (8192*x2)), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 1024, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr3 + (x0 + (16*((-512) + x1)) + (8192*x2)), tmp14, other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6w/c6wr4loz4rxs56x2er2z7yyokvbxpzsmffbuifvku2xqaerh75p3.py
# Topologically Sorted Source Nodes: [x_19], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_19 => convert_element_type_5
# Graph fragment:
#   %convert_element_type_5 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_2, torch.int64), kwargs = {})
triton_poi_fused__to_copy_18 = async_compile.triton('triton_poi_fused__to_copy_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_18(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/oq/coq7wg2jvtpm4oc4zm4dvbkwpc5jinzscvm4jrouu3hlob5nd7rs.py
# Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_19 => add_8, clamp_max_4
# Graph fragment:
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_5, 1), kwargs = {})
#   %clamp_max_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_8, 3), kwargs = {})
triton_poi_fused_add_clamp_19 = async_compile.triton('triton_poi_fused_add_clamp_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_19(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rh/crhh5ib7z3hkisro2vncldz577bevkgu7k3u5nnclrqmgo3wnuzx.py
# Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   x_19 => add_7, clamp_max_6, clamp_min_4, clamp_min_6, convert_element_type_4, iota_2, mul_19, sub_7, sub_9
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_2, torch.float32), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.5), kwargs = {})
#   %mul_19 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_7, 0.5), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_19, 0.5), kwargs = {})
#   %clamp_min_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_7, 0.0), kwargs = {})
#   %sub_9 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_4, %convert_element_type_7), kwargs = {})
#   %clamp_min_6 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_9, 0.0), kwargs = {})
#   %clamp_max_6 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_6, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_20(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/qk/cqkzmxtnx34qktmz7vfilm2bzsfk2r5cxo3wx6pwurql3crkpejs.py
# Topologically Sorted Source Nodes: [conv2d_13, x_18, x_19], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_13 => convolution_13
#   x_18 => mul_18, where_13
#   x_19 => _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, add_11, add_12, add_13, mul_21, mul_22, mul_23, sub_10, sub_11, sub_13
# Graph fragment:
#   %convolution_13 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_28, %primals_29, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_18 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_13, 0.1), kwargs = {})
#   %where_13 : [num_users=4] = call_function[target=torch.ops.aten.where.self](args = (%gt_13, %convolution_13, %mul_18), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_13, [None, None, %convert_element_type_5, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_13, [None, None, %convert_element_type_5, %clamp_max_5]), kwargs = {})
#   %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_13, [None, None, %clamp_max_4, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_13, [None, None, %clamp_max_4, %clamp_max_5]), kwargs = {})
#   %sub_10 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_5, %_unsafe_index_4), kwargs = {})
#   %mul_21 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_10, %clamp_max_6), kwargs = {})
#   %add_11 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_4, %mul_21), kwargs = {})
#   %sub_11 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_7, %_unsafe_index_6), kwargs = {})
#   %mul_22 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_11, %clamp_max_6), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_6, %mul_22), kwargs = {})
#   %sub_13 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_12, %add_11), kwargs = {})
#   %mul_23 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_13, %clamp_max_7), kwargs = {})
#   %add_13 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_11, %mul_23), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21', '''
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: '*i64', 2: '*i64', 3: '*i1', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_leaky_relu_mul_sub_21(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x6 = (xindex // 64)
    x2 = (xindex // 64) % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + (4*tmp19) + (16*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + (4*tmp19) + (16*x6)), None, eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + (4*tmp19) + (16*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + (4*tmp19) + (16*x6)), None, eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + (x4), tmp49, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vn/cvntemv5weoouv65lvun2muyb6apyj7dkrnebouaithxvdyd4hl4.py
# Topologically Sorted Source Nodes: [conv2d_14, x_20], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_14 => convolution_14
#   x_20 => gt_14
# Graph fragment:
#   %convolution_14 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_13, %primals_30, %primals_31, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_14 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_14, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_22 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_22', '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_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/3p/c3pazcbmhoubkrcj7s65glics5kpj5vv7x2zlnvymydp46fxyf2m.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_14, %where_7], 1), kwargs = {})
triton_poi_fused_cat_23 = async_compile.triton('triton_poi_fused_cat_23', '''
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: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_23', '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_cat_23(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + (64*x1) + (16384*x2)), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 512, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr3 + (x0 + (64*((-256) + x1)) + (16384*x2)), tmp14, other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/mi/cmiwjqieuspwn256jnrugfvht2dt7ofln2psibayqc3twrtpkngi.py
# Topologically Sorted Source Nodes: [x_22], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_22 => convert_element_type_9
# Graph fragment:
#   %convert_element_type_9 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_4, torch.int64), kwargs = {})
triton_poi_fused__to_copy_24 = async_compile.triton('triton_poi_fused__to_copy_24', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_24(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ki/ckith5u474vepvwaijseaqbn665u5jpg5stc4cj42bnzsgj6uexm.py
# Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_22 => add_15, clamp_max_8
# Graph fragment:
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_9, 1), kwargs = {})
#   %clamp_max_8 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_15, 7), kwargs = {})
triton_poi_fused_add_clamp_25 = async_compile.triton('triton_poi_fused_add_clamp_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_25(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 7, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lu/cluyprd6omil4csahwtbdldnx2kt7j7znt35dzjdzj4xcxjsppaa.py
# Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   x_22 => add_14, clamp_max_10, clamp_min_10, clamp_min_8, convert_element_type_8, iota_4, mul_26, sub_14, sub_16
# Graph fragment:
#   %iota_4 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_4, torch.float32), kwargs = {})
#   %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.5), kwargs = {})
#   %mul_26 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_14, 0.5), kwargs = {})
#   %sub_14 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_26, 0.5), kwargs = {})
#   %clamp_min_8 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_14, 0.0), kwargs = {})
#   %sub_16 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_8, %convert_element_type_11), kwargs = {})
#   %clamp_min_10 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_16, 0.0), kwargs = {})
#   %clamp_max_10 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_10, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_26(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/qc/cqcxmomsd2pfozktj753ije5uupmwdotvhhglolxtdikeyegh5yz.py
# Topologically Sorted Source Nodes: [conv2d_15, x_21, x_22], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   x_21 => mul_25, where_15
#   x_22 => _unsafe_index_10, _unsafe_index_11, _unsafe_index_8, _unsafe_index_9, add_18, add_19, add_20, mul_28, mul_29, mul_30, sub_17, sub_18, sub_20
# Graph fragment:
#   %convolution_15 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_32, %primals_33, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_25 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_15, 0.1), kwargs = {})
#   %where_15 : [num_users=4] = call_function[target=torch.ops.aten.where.self](args = (%gt_15, %convolution_15, %mul_25), kwargs = {})
#   %_unsafe_index_8 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_15, [None, None, %convert_element_type_9, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_9 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_15, [None, None, %convert_element_type_9, %clamp_max_9]), kwargs = {})
#   %_unsafe_index_10 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_15, [None, None, %clamp_max_8, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_15, [None, None, %clamp_max_8, %clamp_max_9]), kwargs = {})
#   %sub_17 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_9, %_unsafe_index_8), kwargs = {})
#   %mul_28 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_17, %clamp_max_10), kwargs = {})
#   %add_18 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_8, %mul_28), kwargs = {})
#   %sub_18 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_11, %_unsafe_index_10), kwargs = {})
#   %mul_29 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_18, %clamp_max_10), kwargs = {})
#   %add_19 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_10, %mul_29), kwargs = {})
#   %sub_20 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_19, %add_18), kwargs = {})
#   %mul_30 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_20, %clamp_max_11), kwargs = {})
#   %add_20 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_18, %mul_30), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27', '''
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: '*i64', 2: '*i64', 3: '*i1', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_leaky_relu_mul_sub_27(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x6 = (xindex // 256)
    x2 = (xindex // 256) % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + (8*tmp19) + (64*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + (8*tmp19) + (64*x6)), None, eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + (8*tmp19) + (64*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + (8*tmp19) + (64*x6)), None, eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + (x4), tmp49, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2n/c2ncutq26bahxlgkdh4rlnvyr47bwimc4zzutvjxr5n6y6efndwb.py
# Topologically Sorted Source Nodes: [conv2d_16, x_23], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_16 => convolution_16
#   x_23 => gt_16
# Graph fragment:
#   %convolution_16 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_20, %primals_34, %primals_35, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_16 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_16, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_28 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_28', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_28', '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_28(in_ptr0, in_ptr1, 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)
    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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/oe/coeffqdqijre65ihx7pvd5fl3wkvhaztzhmg43n5sxftyfhfnesu.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_16, %where_5], 1), kwargs = {})
triton_poi_fused_cat_29 = async_compile.triton('triton_poi_fused_cat_29', '''
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: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_29', '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_cat_29(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + (256*x1) + (32768*x2)), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 256, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr3 + (x0 + (256*((-128) + x1)) + (32768*x2)), tmp14, other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kk/ckknliedqrn55tdjnurtw2wmbhy4m7nftlest3rkcxytrug6sjjb.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_25 => convert_element_type_13
# Graph fragment:
#   %convert_element_type_13 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_6, torch.int64), kwargs = {})
triton_poi_fused__to_copy_30 = async_compile.triton('triton_poi_fused__to_copy_30', '''
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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_30', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_30(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/yi/cyid3ait3xdgmowp4yeresvz4pwdwiylxhglyhgg7hauo5drkgwr.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_25 => add_22, clamp_max_12
# Graph fragment:
#   %add_22 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_13, 1), kwargs = {})
#   %clamp_max_12 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_22, 15), kwargs = {})
triton_poi_fused_add_clamp_31 = async_compile.triton('triton_poi_fused_add_clamp_31', '''
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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_31', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_31(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 15, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2b/c2bhdwgkdtsx66umnkggdw7khh2c5wl4ogab34mcyesmsvh7u75z.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   x_25 => add_21, clamp_max_14, clamp_min_12, clamp_min_14, convert_element_type_12, iota_6, mul_33, sub_21, sub_23
# Graph fragment:
#   %iota_6 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_12 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_6, torch.float32), kwargs = {})
#   %add_21 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_12, 0.5), kwargs = {})
#   %mul_33 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_21, 0.5), kwargs = {})
#   %sub_21 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_33, 0.5), kwargs = {})
#   %clamp_min_12 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_21, 0.0), kwargs = {})
#   %sub_23 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_12, %convert_element_type_15), kwargs = {})
#   %clamp_min_14 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_23, 0.0), kwargs = {})
#   %clamp_max_14 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_14, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32', '''
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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_32(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/74/c74xhgb6wezra6e6cyzbt6yotgcl7i7n4p3es6nogdaczlcdlrl4.py
# Topologically Sorted Source Nodes: [conv2d_17, x_24, x_25], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_17 => convolution_17
#   x_24 => mul_32, where_17
#   x_25 => _unsafe_index_12, _unsafe_index_13, _unsafe_index_14, _unsafe_index_15, add_25, add_26, add_27, mul_35, mul_36, mul_37, sub_24, sub_25, sub_27
# Graph fragment:
#   %convolution_17 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_36, %primals_37, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_32 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_17, 0.1), kwargs = {})
#   %where_17 : [num_users=4] = call_function[target=torch.ops.aten.where.self](args = (%gt_17, %convolution_17, %mul_32), kwargs = {})
#   %_unsafe_index_12 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_17, [None, None, %convert_element_type_13, %convert_element_type_15]), kwargs = {})
#   %_unsafe_index_13 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_17, [None, None, %convert_element_type_13, %clamp_max_13]), kwargs = {})
#   %_unsafe_index_14 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_17, [None, None, %clamp_max_12, %convert_element_type_15]), kwargs = {})
#   %_unsafe_index_15 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_17, [None, None, %clamp_max_12, %clamp_max_13]), kwargs = {})
#   %sub_24 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_13, %_unsafe_index_12), kwargs = {})
#   %mul_35 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_24, %clamp_max_14), kwargs = {})
#   %add_25 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_12, %mul_35), kwargs = {})
#   %sub_25 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_15, %_unsafe_index_14), kwargs = {})
#   %mul_36 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_25, %clamp_max_14), kwargs = {})
#   %add_26 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_14, %mul_36), kwargs = {})
#   %sub_27 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_26, %add_25), kwargs = {})
#   %mul_37 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_27, %clamp_max_15), kwargs = {})
#   %add_27 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_25, %mul_37), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33', '''
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: '*i64', 2: '*i64', 3: '*i1', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_leaky_relu_mul_sub_33(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 32) % 32
    x0 = xindex % 32
    x6 = (xindex // 1024)
    x2 = (xindex // 1024) % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (16*tmp4) + (256*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + (16*tmp4) + (256*x6)), None, eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + (16*tmp19) + (256*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + (16*tmp19) + (256*x6)), None, eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + (16*tmp19) + (256*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + (16*tmp19) + (256*x6)), None, eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + (16*tmp4) + (256*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + (16*tmp4) + (256*x6)), None, eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + (x4), tmp49, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/hu/chuh444ehiqujq3pobg3s6kf4jk3jfs66ff4yxzuqyv7z7gvdw4l.py
# Topologically Sorted Source Nodes: [conv2d_18, x_26], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   x_26 => gt_18
# Graph fragment:
#   %convolution_18 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_27, %primals_38, %primals_39, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_18 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_18, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_34 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_34', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_34', '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_34(in_ptr0, in_ptr1, 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)
    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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/wo/cwov5xjz2rgypru6odo5shttzkvjzbv2j5h765xadmngxefsg27w.py
# Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_18, %where_3], 1), kwargs = {})
triton_poi_fused_cat_35 = async_compile.triton('triton_poi_fused_cat_35', '''
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: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_35', '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_cat_35(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + (1024*x1) + (65536*x2)), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 128, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr3 + (x0 + (1024*((-64) + x1)) + (65536*x2)), tmp14, other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/dy/cdyi77rlf5lxwibfyd5p2m432vdqftcfdpn6tuqyv7hyajbxkkvo.py
# Topologically Sorted Source Nodes: [x_28], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_28 => convert_element_type_17
# Graph fragment:
#   %convert_element_type_17 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_8, torch.int64), kwargs = {})
triton_poi_fused__to_copy_36 = async_compile.triton('triton_poi_fused__to_copy_36', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_36', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_36(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/l5/cl5xfazaijdiktz5n5gqb2xvmg6f5wpggcjdlnx676ib5wqnt2bo.py
# Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_28 => add_29, clamp_max_16
# Graph fragment:
#   %add_29 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_17, 1), kwargs = {})
#   %clamp_max_16 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_29, 31), kwargs = {})
triton_poi_fused_add_clamp_37 = async_compile.triton('triton_poi_fused_add_clamp_37', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_37', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_37(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 31, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/zw/czwrbkjbdq3qzzeebsz3vxkktcfyv5fp5csjdanc2n4yhokgkzxs.py
# Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   x_28 => add_28, clamp_max_18, clamp_min_16, clamp_min_18, convert_element_type_16, iota_8, mul_40, sub_28, sub_30
# Graph fragment:
#   %iota_8 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_16 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_8, torch.float32), kwargs = {})
#   %add_28 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_16, 0.5), kwargs = {})
#   %mul_40 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_28, 0.5), kwargs = {})
#   %sub_28 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_40, 0.5), kwargs = {})
#   %clamp_min_16 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_28, 0.0), kwargs = {})
#   %sub_30 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_16, %convert_element_type_19), kwargs = {})
#   %clamp_min_18 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_30, 0.0), kwargs = {})
#   %clamp_max_18 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_18, 1.0), kwargs = {})
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__to_copy_add_arange_clamp_mul_sub_38(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cj/ccjteqzvlszshm7xogphjn6lezrnhtguz6t2ft3y2qrajrcko2wk.py
# Topologically Sorted Source Nodes: [conv2d_19, x_27, x_28], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_19 => convolution_19
#   x_27 => mul_39, where_19
#   x_28 => _unsafe_index_16, _unsafe_index_17, _unsafe_index_18, _unsafe_index_19, add_32, add_33, add_34, mul_42, mul_43, mul_44, sub_31, sub_32, sub_34
# Graph fragment:
#   %convolution_19 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_40, %primals_41, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_39 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_19, 0.1), kwargs = {})
#   %where_19 : [num_users=4] = call_function[target=torch.ops.aten.where.self](args = (%gt_19, %convolution_19, %mul_39), kwargs = {})
#   %_unsafe_index_16 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_19, [None, None, %convert_element_type_17, %convert_element_type_19]), kwargs = {})
#   %_unsafe_index_17 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_19, [None, None, %convert_element_type_17, %clamp_max_17]), kwargs = {})
#   %_unsafe_index_18 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_19, [None, None, %clamp_max_16, %convert_element_type_19]), kwargs = {})
#   %_unsafe_index_19 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_19, [None, None, %clamp_max_16, %clamp_max_17]), kwargs = {})
#   %sub_31 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_17, %_unsafe_index_16), kwargs = {})
#   %mul_42 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_31, %clamp_max_18), kwargs = {})
#   %add_32 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_16, %mul_42), kwargs = {})
#   %sub_32 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_19, %_unsafe_index_18), kwargs = {})
#   %mul_43 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_32, %clamp_max_18), kwargs = {})
#   %add_33 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_18, %mul_43), kwargs = {})
#   %sub_34 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_33, %add_32), kwargs = {})
#   %mul_44 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_34, %clamp_max_19), kwargs = {})
#   %add_34 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_32, %mul_44), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39', '''
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: '*i64', 2: '*i64', 3: '*i1', 4: '*fp32', 5: '*fp32', 6: '*i64', 7: '*i64', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 7, '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__unsafe_index_add_convolution_leaky_relu_mul_sub_39(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 64
    x0 = xindex % 64
    x6 = (xindex // 4096)
    x2 = (xindex // 4096) % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + (x2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + (x0), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 32, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (32*tmp4) + (1024*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + (32*tmp4) + (1024*x6)), None, eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + (32*tmp19) + (1024*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + (32*tmp19) + (1024*x6)), None, eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + (32*tmp19) + (1024*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + (32*tmp19) + (1024*x6)), None, eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + (32*tmp4) + (1024*x6)), None, eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + (32*tmp4) + (1024*x6)), None, eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + (x4), tmp49, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vz/cvzt4swwgye3zpxep4ligqcdlu5xxf7fecsvlec4qsz3qtn6tkxy.py
# Topologically Sorted Source Nodes: [conv2d_20, x_29], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_20 => convolution_20
#   x_29 => gt_20
# Graph fragment:
#   %convolution_20 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_34, %primals_42, %primals_43, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_20 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_20, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_40 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_40', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_40', '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_40(in_ptr0, in_ptr1, 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)
    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
    tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rm/crmdmqyxav4fb4725fm2hhwf6m4yrxuhqmo2dbcjkiu6gomd2akp.py
# Topologically Sorted Source Nodes: [cat_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_4 => cat_4
# Graph fragment:
#   %cat_4 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_20, %where_1], 1), kwargs = {})
triton_poi_fused_cat_41 = async_compile.triton('triton_poi_fused_cat_41', '''
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: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_41', '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_cat_41(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + (4096*x1) + (131072*x2)), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 64, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr3 + (x0 + (4096*((-32) + x1)) + (131072*x2)), tmp14, other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + (x3), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/p7/cp74qdkmdadhqce4dzechhvpihfuica6bbejv65ptme5otg3jhj3.py
# Topologically Sorted Source Nodes: [conv2d_22, x_31], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_22 => convolution_22
#   x_31 => gt_22, mul_47, where_22
# Graph fragment:
#   %convolution_22 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_21, %primals_46, %primals_47, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_22 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_22, 0), kwargs = {})
#   %mul_47 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_22, 0.1), kwargs = {})
#   %where_22 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_22, %convolution_22, %mul_47), kwargs = {})
triton_poi_fused_convolution_leaky_relu_42 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_42', '''
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_42', '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_42(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 // 4096) % 4
    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 = 0.1
    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')


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, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 7, 7), (1568, 49, 7, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 5, 5), (1600, 25, 5, 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, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_23, (512, ), (1, ))
    assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (512, ), (1, ))
    assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_27, (512, ), (1, ))
    assert_size_stride(primals_28, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_29, (512, ), (1, ))
    assert_size_stride(primals_30, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (256, ), (1, ))
    assert_size_stride(primals_32, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (256, ), (1, ))
    assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (128, ), (1, ))
    assert_size_stride(primals_36, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (128, ), (1, ))
    assert_size_stride(primals_38, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (64, ), (1, ))
    assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_41, (64, ), (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, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (32, ), (1, ))
    assert_size_stride(primals_46, (4, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (4, ), (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=(3, 3), 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, x], 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=(3, 3), 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, s1], 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)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_1.run(buf5, buf6, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf8 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf7, primals_7, buf8, buf9, 262144, grid=grid(262144), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf11 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        buf12 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, x_3], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf10, primals_9, buf11, buf12, 262144, grid=grid(262144), stream=stream0)
        del primals_9
        buf13 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_3.run(buf12, buf13, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf15 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf16 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_4, x_5], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf14, primals_11, buf15, buf16, 131072, grid=grid(131072), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        buf19 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, x_6], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf17, primals_13, buf18, buf19, 131072, grid=grid(131072), stream=stream0)
        del primals_13
        buf20 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_5.run(buf19, buf20, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf21 = extern_kernels.convolution(buf20, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf22 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        buf23 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_6, x_8], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf21, primals_15, buf22, buf23, 65536, grid=grid(65536), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_16, 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 = buf21; del buf21  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, x_9], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_6.run(buf24, primals_17, buf25, buf26, 65536, grid=grid(65536), stream=stream0)
        del primals_17
        buf27 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_7.run(buf26, buf27, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf29 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        buf30 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_8, x_11], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf28, primals_19, buf29, buf30, 32768, grid=grid(32768), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf31 = extern_kernels.convolution(buf30, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf32 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        buf33 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, x_12], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_8.run(buf31, primals_21, buf32, buf33, 32768, grid=grid(32768), stream=stream0)
        del primals_21
        buf34 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_9.run(buf33, buf34, 8192, grid=grid(8192), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf35 = extern_kernels.convolution(buf34, primals_22, 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, 2, 2), (2048, 4, 2, 1))
        buf36 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
        buf37 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_10, x_14], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_10.run(buf35, primals_23, buf36, buf37, 8192, grid=grid(8192), stream=stream0)
        del buf35
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 2, 2), (2048, 4, 2, 1))
        buf39 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_11, x_15], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_11.run(buf38, primals_25, buf39, 8192, grid=grid(8192), stream=stream0)
        buf40 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_12.run(buf40, 4, grid=grid(4), stream=stream0)
        buf41 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_13.run(buf41, 4, grid=grid(4), stream=stream0)
        buf42 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_12.run(buf42, 4, grid=grid(4), stream=stream0)
        buf43 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_13.run(buf43, 4, grid=grid(4), stream=stream0)
        buf46 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14.run(buf46, 4, grid=grid(4), stream=stream0)
        buf48 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14.run(buf48, 4, grid=grid(4), stream=stream0)
        buf45 = buf31; del buf31  # reuse
        buf49 = buf45; del buf45  # reuse
        buf50 = buf49; del buf49  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, x_15, x_16], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15.run(buf50, buf41, buf42, buf39, buf38, primals_25, buf40, buf43, buf46, buf48, 32768, grid=grid(32768), stream=stream0)
        del buf38
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf52 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_12, x_17], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_16.run(buf51, primals_27, buf52, 32768, grid=grid(32768), stream=stream0)
        buf53 = reinterpret_tensor(buf24, (4, 1024, 4, 4), (16384, 16, 4, 1), 0); del buf24  # reuse
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_17.run(buf52, buf51, primals_27, buf33, buf53, 65536, grid=grid(65536), stream=stream0)
        del buf51
        del primals_27
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf55 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_13, x_18], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_16.run(buf54, primals_29, buf55, 32768, grid=grid(32768), stream=stream0)
        buf56 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_18.run(buf56, 8, grid=grid(8), stream=stream0)
        buf57 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_19.run(buf57, 8, grid=grid(8), stream=stream0)
        buf58 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_18.run(buf58, 8, grid=grid(8), stream=stream0)
        buf59 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_19.run(buf59, 8, grid=grid(8), stream=stream0)
        buf62 = empty_strided_cuda((8, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20.run(buf62, 8, grid=grid(8), stream=stream0)
        buf64 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20.run(buf64, 8, grid=grid(8), stream=stream0)
        buf61 = reinterpret_tensor(buf17, (4, 512, 8, 8), (32768, 64, 8, 1), 0); del buf17  # reuse
        buf65 = buf61; del buf61  # reuse
        buf66 = buf65; del buf65  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, x_18, x_19], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21.run(buf66, buf57, buf58, buf55, buf54, primals_29, buf56, buf59, buf62, buf64, 131072, grid=grid(131072), stream=stream0)
        del buf54
        del primals_29
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf67 = extern_kernels.convolution(buf66, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf68 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_14, x_20], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_22.run(buf67, primals_31, buf68, 65536, grid=grid(65536), stream=stream0)
        buf69 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_23.run(buf68, buf67, primals_31, buf26, buf69, 131072, grid=grid(131072), stream=stream0)
        del buf67
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf70 = extern_kernels.convolution(buf69, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf71 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_15, x_21], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_22.run(buf70, primals_33, buf71, 65536, grid=grid(65536), stream=stream0)
        buf72 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_24.run(buf72, 16, grid=grid(16), stream=stream0)
        buf73 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_25.run(buf73, 16, grid=grid(16), stream=stream0)
        buf74 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_24.run(buf74, 16, grid=grid(16), stream=stream0)
        buf75 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_25.run(buf75, 16, grid=grid(16), stream=stream0)
        buf78 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26.run(buf78, 16, grid=grid(16), stream=stream0)
        buf80 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26.run(buf80, 16, grid=grid(16), stream=stream0)
        buf77 = reinterpret_tensor(buf10, (4, 256, 16, 16), (65536, 256, 16, 1), 0); del buf10  # reuse
        buf81 = buf77; del buf77  # reuse
        buf82 = buf81; del buf81  # reuse
        # Topologically Sorted Source Nodes: [conv2d_15, x_21, x_22], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27.run(buf82, buf73, buf74, buf71, buf70, primals_33, buf72, buf75, buf78, buf80, 262144, grid=grid(262144), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf83 = extern_kernels.convolution(buf82, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf84 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_16, x_23], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_28.run(buf83, primals_35, buf84, 131072, grid=grid(131072), stream=stream0)
        buf85 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_29.run(buf84, buf83, primals_35, buf19, buf85, 262144, grid=grid(262144), stream=stream0)
        del buf83
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf86 = extern_kernels.convolution(buf85, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf86, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf87 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_17, x_24], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_28.run(buf86, primals_37, buf87, 131072, grid=grid(131072), stream=stream0)
        buf88 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_30.run(buf88, 32, grid=grid(32), stream=stream0)
        buf89 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_31.run(buf89, 32, grid=grid(32), stream=stream0)
        buf90 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_30.run(buf90, 32, grid=grid(32), stream=stream0)
        buf91 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_31.run(buf91, 32, grid=grid(32), stream=stream0)
        buf94 = empty_strided_cuda((32, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32.run(buf94, 32, grid=grid(32), stream=stream0)
        buf96 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32.run(buf96, 32, grid=grid(32), stream=stream0)
        buf93 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 32, 1), 0); del buf3  # reuse
        buf97 = buf93; del buf93  # reuse
        buf98 = buf97; del buf97  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17, x_24, x_25], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33.run(buf98, buf89, buf90, buf87, buf86, primals_37, buf88, buf91, buf94, buf96, 524288, grid=grid(524288), stream=stream0)
        del buf86
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf99 = extern_kernels.convolution(buf98, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf99, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf100 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_18, x_26], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_34.run(buf99, primals_39, buf100, 262144, grid=grid(262144), stream=stream0)
        buf101 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_35.run(buf100, buf99, primals_39, buf12, buf101, 524288, grid=grid(524288), stream=stream0)
        del buf99
        del primals_39
        # Topologically Sorted Source Nodes: [conv2d_19], Original ATen: [aten.convolution]
        buf102 = extern_kernels.convolution(buf101, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf102, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf103 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_19, x_27], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_34.run(buf102, primals_41, buf103, 262144, grid=grid(262144), stream=stream0)
        buf104 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_36.run(buf104, 64, grid=grid(64), stream=stream0)
        buf105 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_37.run(buf105, 64, grid=grid(64), stream=stream0)
        buf106 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_36.run(buf106, 64, grid=grid(64), stream=stream0)
        buf107 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_37.run(buf107, 64, grid=grid(64), stream=stream0)
        buf110 = empty_strided_cuda((64, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38.run(buf110, 64, grid=grid(64), stream=stream0)
        buf112 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38.run(buf112, 64, grid=grid(64), stream=stream0)
        buf109 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        buf113 = buf109; del buf109  # reuse
        buf114 = buf113; del buf113  # reuse
        # Topologically Sorted Source Nodes: [conv2d_19, x_27, x_28], Original ATen: [aten.convolution, aten.leaky_relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39.run(buf114, buf105, buf106, buf103, buf102, primals_41, buf104, buf107, buf110, buf112, 1048576, grid=grid(1048576), stream=stream0)
        del buf102
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf115 = extern_kernels.convolution(buf114, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf115, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf116 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_20, x_29], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_40.run(buf115, primals_43, buf116, 524288, grid=grid(524288), stream=stream0)
        buf117 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_41.run(buf116, buf115, primals_43, buf5, buf117, 1048576, grid=grid(1048576), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf118 = extern_kernels.convolution(buf117, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf118, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf119 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        buf120 = buf115; del buf115  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21, x_30], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf118, primals_45, buf119, buf120, 524288, grid=grid(524288), stream=stream0)
        del buf118
        del primals_45
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf121 = extern_kernels.convolution(buf120, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf121, (4, 4, 64, 64), (16384, 4096, 64, 1))
        buf122 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1), torch.bool)
        buf123 = reinterpret_tensor(buf70, (4, 4, 64, 64), (16384, 4096, 64, 1), 0); del buf70  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22, x_31], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_42.run(buf121, primals_47, buf122, buf123, 65536, grid=grid(65536), stream=stream0)
        del buf121
        del primals_47
    return (buf123, 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, buf8, buf9, buf11, buf12, buf13, buf15, buf16, buf18, buf19, buf20, buf22, buf23, buf25, buf26, buf27, buf29, buf30, buf32, buf33, buf34, buf36, buf37, buf39, buf40, buf41, buf42, buf43, buf46, buf48, buf50, buf52, buf53, buf55, buf56, buf57, buf58, buf59, buf62, buf64, buf66, buf68, buf69, buf71, buf72, buf73, buf74, buf75, buf78, buf80, buf82, buf84, buf85, buf87, buf88, buf89, buf90, buf91, buf94, buf96, buf98, buf100, buf101, buf103, buf104, buf105, buf106, buf107, buf110, buf112, buf114, buf116, buf117, buf119, buf120, buf122, )


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, 4, 7, 7), (196, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 7, 7), (1568, 49, 7, 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, 5, 5), (800, 25, 5, 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, 5, 5), (1600, 25, 5, 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, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((512, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((64, 128, 3, 3), (1152, 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, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((64, ), (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, 64, 3, 3), (576, 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((4, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((4, ), (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 down(nn.Module):

    def __init__(self, inChannels, outChannels, filterSize):
        super(down, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, outChannels, filterSize, stride=
            1, padding=int((filterSize - 1) / 2))
        self.conv2 = nn.Conv2d(outChannels, outChannels, filterSize, stride
            =1, padding=int((filterSize - 1) / 2))

    def forward(self, x):
        x = F.avg_pool2d(x, 2)
        x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
        x = F.leaky_relu(self.conv2(x), negative_slope=0.1)
        return x


class up(nn.Module):

    def __init__(self, inChannels, outChannels):
        super(up, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, outChannels, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(2 * outChannels, outChannels, 3, stride=1,
            padding=1)

    def forward(self, x, skpCn):
        x = F.interpolate(x, scale_factor=2, mode='bilinear')
        x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
        x = F.leaky_relu(self.conv2(torch.cat((x, skpCn), 1)),
            negative_slope=0.1)
        return x


class UNet(nn.Module):

    def __init__(self, inChannels, outChannels):
        super(UNet, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, 32, 7, stride=1, padding=3)
        self.conv2 = nn.Conv2d(32, 32, 7, stride=1, padding=3)
        self.down1 = down(32, 64, 5)
        self.down2 = down(64, 128, 3)
        self.down3 = down(128, 256, 3)
        self.down4 = down(256, 512, 3)
        self.down5 = down(512, 512, 3)
        self.up1 = up(512, 512)
        self.up2 = up(512, 256)
        self.up3 = up(256, 128)
        self.up4 = up(128, 64)
        self.up5 = up(64, 32)
        self.conv3 = nn.Conv2d(32, outChannels, 3, stride=1, padding=1)

    def forward(self, x):
        x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
        s1 = F.leaky_relu(self.conv2(x), negative_slope=0.1)
        s2 = self.down1(s1)
        s3 = self.down2(s2)
        s4 = self.down3(s3)
        s5 = self.down4(s4)
        x = self.down5(s5)
        x = self.up1(x, s5)
        x = self.up2(x, s4)
        x = self.up3(x, s3)
        x = self.up4(x, s2)
        x = self.up5(x, s1)
        x = F.leaky_relu(self.conv3(x), negative_slope=0.1)
        return x


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
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 = 0.1
    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_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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 = 0.1
    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_avg_pool2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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 = 0.1
    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_avg_pool2d_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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 = 0.1
    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_avg_pool2d_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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 = 0.1
    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_avg_pool2d_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), None, eviction_policy=
        'evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_10(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 // 4 % 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 = 0.1
    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_convolution_leaky_relu_11(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4 % 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_12(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_13(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = triton_helpers.minimum(tmp10, tmp9)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15(
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x6 = xindex // 16
    x2 = xindex // 16 % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 2, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + 2 * tmp19 + 4 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + 2 * tmp19 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + 2 * tmp19 + 4 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + 2 * tmp19 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + 2 * tmp4 + 4 * x6), None,
        eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + x4, tmp49, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_16(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 16 % 1024
    x0 = xindex % 16
    x2 = xindex // 16384
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 8192 * x2), tmp4, other=0.0).to(tl
        .int1)
    tmp6 = tl.load(in_ptr1 + (x0 + 16 * x1 + 8192 * x2), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 1024, tl.int64)
    tmp17 = tl.load(in_ptr3 + (x0 + 16 * (-512 + x1) + 8192 * x2), tmp14,
        other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + x3, tmp18, None)


@triton.jit
def triton_poi_fused__to_copy_18(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_19(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21(
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x6 = xindex // 64
    x2 = xindex // 64 % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + 4 * tmp19 + 16 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + 4 * tmp19 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + 4 * tmp19 + 16 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + 4 * tmp19 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + x4, tmp49, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_22(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(
        tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + 64 * x1 + 16384 * x2), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp17 = tl.load(in_ptr3 + (x0 + 64 * (-256 + x1) + 16384 * x2), tmp14,
        other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + x3, tmp18, None)


@triton.jit
def triton_poi_fused__to_copy_24(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_25(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 7, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27(
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x6 = xindex // 256
    x2 = xindex // 256 % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + 8 * tmp19 + 64 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + 8 * tmp19 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + 8 * tmp19 + 64 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + 8 * tmp19 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + x4, tmp49, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_28(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_29(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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).to(
        tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + 256 * x1 + 32768 * x2), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp17 = tl.load(in_ptr3 + (x0 + 256 * (-128 + x1) + 32768 * x2), tmp14,
        other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + x3, tmp18, None)


@triton.jit
def triton_poi_fused__to_copy_30(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_31(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 15, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33(
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 32 % 32
    x0 = xindex % 32
    x6 = xindex // 1024
    x2 = xindex // 1024 % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 16 * tmp4 + 256 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + 16 * tmp4 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + 16 * tmp19 + 256 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + 16 * tmp19 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + 16 * tmp19 + 256 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + 16 * tmp19 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + 16 * tmp4 + 256 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + 16 * tmp4 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + x4, tmp49, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_34(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_35(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
        ).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + 1024 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp17 = tl.load(in_ptr3 + (x0 + 1024 * (-64 + x1) + 65536 * x2), tmp14,
        other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + x3, tmp18, None)


@triton.jit
def triton_poi_fused__to_copy_36(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_37(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 31, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39(
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
    in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 64
    x0 = xindex % 64
    x6 = xindex // 4096
    x2 = xindex // 4096 % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 32, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 32 * tmp4 + 1024 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp10 = tl.load(in_ptr3 + (tmp8 + 32 * tmp4 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp12 = tmp10 + tmp11
    tmp13 = 0.1
    tmp14 = tmp12 * tmp13
    tmp15 = tl.where(tmp9, tmp12, tmp14)
    tmp17 = tmp16 + tmp1
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr2 + (tmp8 + 32 * tmp19 + 1024 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr3 + (tmp8 + 32 * tmp19 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp22 = tmp21 + tmp11
    tmp23 = tmp22 * tmp13
    tmp24 = tl.where(tmp20, tmp22, tmp23)
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp28 + 32 * tmp19 + 1024 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr3 + (tmp28 + 32 * tmp19 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp31 = tmp30 + tmp11
    tmp32 = tmp31 * tmp13
    tmp33 = tl.where(tmp29, tmp31, tmp32)
    tmp34 = tmp33 - tmp24
    tmp36 = tmp34 * tmp35
    tmp37 = tmp24 + tmp36
    tmp38 = tl.load(in_ptr2 + (tmp28 + 32 * tmp4 + 1024 * x6), None,
        eviction_policy='evict_last').to(tl.int1)
    tmp39 = tl.load(in_ptr3 + (tmp28 + 32 * tmp4 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp40 = tmp39 + tmp11
    tmp41 = tmp40 * tmp13
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp42 - tmp15
    tmp44 = tmp43 * tmp35
    tmp45 = tmp15 + tmp44
    tmp46 = tmp45 - tmp37
    tmp48 = tmp46 * tmp47
    tmp49 = tmp37 + tmp48
    tl.store(in_out_ptr1 + x4, tmp49, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_40(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 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
    tl.store(out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_41(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
        ).to(tl.int1)
    tmp6 = tl.load(in_ptr1 + (x0 + 4096 * x1 + 131072 * x2), tmp4, other=0.0)
    tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = 0.1
    tmp10 = tmp8 * tmp9
    tmp11 = tl.where(tmp5, tmp8, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 64, tl.int64)
    tmp17 = tl.load(in_ptr3 + (x0 + 4096 * (-32 + x1) + 131072 * x2), tmp14,
        other=0.0)
    tmp18 = tl.where(tmp4, tmp13, tmp17)
    tl.store(out_ptr0 + x3, tmp18, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_42(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 % 4
    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 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 7, 7), (1568, 49, 7, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 5, 5), (1600, 25, 5, 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, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_23, (512,), (1,))
    assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (512,), (1,))
    assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_27, (512,), (1,))
    assert_size_stride(primals_28, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_29, (512,), (1,))
    assert_size_stride(primals_30, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (256,), (1,))
    assert_size_stride(primals_32, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (256,), (1,))
    assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (128,), (1,))
    assert_size_stride(primals_36, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (128,), (1,))
    assert_size_stride(primals_38, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (64,), (1,))
    assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_41, (64,), (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, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (32,), (1,))
    assert_size_stride(primals_46, (4, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_47, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(3, 3), 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=1024, num_warps=4,
            num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(3, 3), 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=1024, num_warps=4,
            num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(131072)](buf5, buf6, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf8 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf7,
            primals_7, buf8, buf9, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_7
        buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf11 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        buf12 = buf7
        del buf7
        triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf10,
            primals_9, buf11, buf12, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_9
        buf13 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_3[grid(65536)](buf12, buf13, 65536,
            XBLOCK=512, num_warps=4, num_stages=1)
        buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf15 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf16 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf14,
            primals_11, buf15, buf16, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_11
        buf17 = extern_kernels.convolution(buf16, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        buf19 = buf14
        del buf14
        triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf17,
            primals_13, buf18, buf19, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_13
        buf20 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        triton_poi_fused_avg_pool2d_5[grid(32768)](buf19, buf20, 32768,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf21 = extern_kernels.convolution(buf20, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf22 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        buf23 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .float32)
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf21,
            primals_15, buf22, buf23, 65536, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_15
        buf24 = extern_kernels.convolution(buf23, primals_16, 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 = buf21
        del buf21
        triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf24,
            primals_17, buf25, buf26, 65536, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_17
        buf27 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused_avg_pool2d_7[grid(16384)](buf26, buf27, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf28 = extern_kernels.convolution(buf27, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf29 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        buf30 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf28,
            primals_19, buf29, buf30, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_19
        buf31 = extern_kernels.convolution(buf30, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf32 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        buf33 = buf28
        del buf28
        triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf31,
            primals_21, buf32, buf33, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_21
        buf34 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
            float32)
        triton_poi_fused_avg_pool2d_9[grid(8192)](buf33, buf34, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf35 = extern_kernels.convolution(buf34, primals_22, 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, 2, 2), (2048, 4, 2, 1))
        buf36 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
        buf37 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
            float32)
        triton_poi_fused_convolution_leaky_relu_10[grid(8192)](buf35,
            primals_23, buf36, buf37, 8192, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf35
        del primals_23
        buf38 = extern_kernels.convolution(buf37, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 2, 2), (2048, 4, 2, 1))
        buf39 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_11[grid(8192)](buf38,
            primals_25, buf39, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        buf40 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_12[grid(4)](buf40, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf41 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_13[grid(4)](buf41, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf42 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused__to_copy_12[grid(4)](buf42, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        buf43 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_add_clamp_13[grid(4)](buf43, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf46 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14[grid(4)](buf46,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf48 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14[grid(4)](buf48,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf45 = buf31
        del buf31
        buf49 = buf45
        del buf45
        buf50 = buf49
        del buf49
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15[
            grid(32768)](buf50, buf41, buf42, buf39, buf38, primals_25,
            buf40, buf43, buf46, buf48, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf38
        del primals_25
        buf51 = extern_kernels.convolution(buf50, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf52 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        triton_poi_fused_convolution_leaky_relu_16[grid(32768)](buf51,
            primals_27, buf52, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        buf53 = reinterpret_tensor(buf24, (4, 1024, 4, 4), (16384, 16, 4, 1), 0
            )
        del buf24
        triton_poi_fused_cat_17[grid(65536)](buf52, buf51, primals_27,
            buf33, buf53, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del buf51
        del primals_27
        buf54 = extern_kernels.convolution(buf53, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf55 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        triton_poi_fused_convolution_leaky_relu_16[grid(32768)](buf54,
            primals_29, buf55, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        buf56 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_18[grid(8)](buf56, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf57 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_19[grid(8)](buf57, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf58 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_18[grid(8)](buf58, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf59 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused_add_clamp_19[grid(8)](buf59, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf62 = empty_strided_cuda((8,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20[grid(8)](buf62,
            8, XBLOCK=8, num_warps=1, num_stages=1)
        buf64 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20[grid(8)](buf64,
            8, XBLOCK=8, num_warps=1, num_stages=1)
        buf61 = reinterpret_tensor(buf17, (4, 512, 8, 8), (32768, 64, 8, 1), 0)
        del buf17
        buf65 = buf61
        del buf61
        buf66 = buf65
        del buf65
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21[
            grid(131072)](buf66, buf57, buf58, buf55, buf54, primals_29,
            buf56, buf59, buf62, buf64, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf54
        del primals_29
        buf67 = extern_kernels.convolution(buf66, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf67, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf68 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_leaky_relu_22[grid(65536)](buf67,
            primals_31, buf68, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf69 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
            .float32)
        triton_poi_fused_cat_23[grid(131072)](buf68, buf67, primals_31,
            buf26, buf69, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del buf67
        del primals_31
        buf70 = extern_kernels.convolution(buf69, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf71 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_leaky_relu_22[grid(65536)](buf70,
            primals_33, buf71, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf72 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_24[grid(16)](buf72, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf73 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_25[grid(16)](buf73, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf74 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_24[grid(16)](buf74, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf75 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_add_clamp_25[grid(16)](buf75, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf78 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26[grid(16)](buf78,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf80 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26[grid(16)](buf80,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf77 = reinterpret_tensor(buf10, (4, 256, 16, 16), (65536, 256, 16,
            1), 0)
        del buf10
        buf81 = buf77
        del buf77
        buf82 = buf81
        del buf81
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27[
            grid(262144)](buf82, buf73, buf74, buf71, buf70, primals_33,
            buf72, buf75, buf78, buf80, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_33
        buf83 = extern_kernels.convolution(buf82, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf84 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        triton_poi_fused_convolution_leaky_relu_28[grid(131072)](buf83,
            primals_35, buf84, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
        buf85 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
            torch.float32)
        triton_poi_fused_cat_29[grid(262144)](buf84, buf83, primals_35,
            buf19, buf85, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del buf83
        del primals_35
        buf86 = extern_kernels.convolution(buf85, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf86, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf87 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.bool)
        triton_poi_fused_convolution_leaky_relu_28[grid(131072)](buf86,
            primals_37, buf87, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
        buf88 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_30[grid(32)](buf88, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf89 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_31[grid(32)](buf89, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf90 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_30[grid(32)](buf90, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf91 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused_add_clamp_31[grid(32)](buf91, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf94 = empty_strided_cuda((32,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32[grid(32)](buf94,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf96 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32[grid(32)](buf96,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf93 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024, 
            32, 1), 0)
        del buf3
        buf97 = buf93
        del buf93
        buf98 = buf97
        del buf97
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33[
            grid(524288)](buf98, buf89, buf90, buf87, buf86, primals_37,
            buf88, buf91, buf94, buf96, 524288, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf86
        del primals_37
        buf99 = extern_kernels.convolution(buf98, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf99, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf100 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        triton_poi_fused_convolution_leaky_relu_34[grid(262144)](buf99,
            primals_39, buf100, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
        buf101 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_cat_35[grid(524288)](buf100, buf99, primals_39,
            buf12, buf101, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del buf99
        del primals_39
        buf102 = extern_kernels.convolution(buf101, primals_40, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf102, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf103 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.bool)
        triton_poi_fused_convolution_leaky_relu_34[grid(262144)](buf102,
            primals_41, buf103, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
        buf104 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_36[grid(64)](buf104, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf105 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_37[grid(64)](buf105, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf106 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused__to_copy_36[grid(64)](buf106, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf107 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused_add_clamp_37[grid(64)](buf107, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf110 = empty_strided_cuda((64,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38[grid(64)](buf110,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf112 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38[grid(64)](buf112,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf109 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        buf113 = buf109
        del buf109
        buf114 = buf113
        del buf113
        triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39[
            grid(1048576)](buf114, buf105, buf106, buf103, buf102,
            primals_41, buf104, buf107, buf110, buf112, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del buf102
        del primals_41
        buf115 = extern_kernels.convolution(buf114, primals_42, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf115, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf116 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_leaky_relu_40[grid(524288)](buf115,
            primals_43, buf116, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        buf117 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_41[grid(1048576)](buf116, buf115, primals_43,
            buf5, buf117, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_43
        buf118 = extern_kernels.convolution(buf117, primals_44, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf118, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf119 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        buf120 = buf115
        del buf115
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf118,
            primals_45, buf119, buf120, 524288, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf118
        del primals_45
        buf121 = extern_kernels.convolution(buf120, primals_46, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf121, (4, 4, 64, 64), (16384, 4096, 64, 1))
        buf122 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.bool)
        buf123 = reinterpret_tensor(buf70, (4, 4, 64, 64), (16384, 4096, 64,
            1), 0)
        del buf70
        triton_poi_fused_convolution_leaky_relu_42[grid(65536)](buf121,
            primals_47, buf122, buf123, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf121
        del primals_47
    return (buf123, 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, buf8, buf9, buf11, buf12, buf13, buf15, buf16, buf18,
        buf19, buf20, buf22, buf23, buf25, buf26, buf27, buf29, buf30,
        buf32, buf33, buf34, buf36, buf37, buf39, buf40, buf41, buf42,
        buf43, buf46, buf48, buf50, buf52, buf53, buf55, buf56, buf57,
        buf58, buf59, buf62, buf64, buf66, buf68, buf69, buf71, buf72,
        buf73, buf74, buf75, buf78, buf80, buf82, buf84, buf85, buf87,
        buf88, buf89, buf90, buf91, buf94, buf96, buf98, buf100, buf101,
        buf103, buf104, buf105, buf106, buf107, buf110, buf112, buf114,
        buf116, buf117, buf119, buf120, buf122)


class down(nn.Module):

    def __init__(self, inChannels, outChannels, filterSize):
        super(down, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, outChannels, filterSize, stride=
            1, padding=int((filterSize - 1) / 2))
        self.conv2 = nn.Conv2d(outChannels, outChannels, filterSize, stride
            =1, padding=int((filterSize - 1) / 2))

    def forward(self, x):
        x = F.avg_pool2d(x, 2)
        x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
        x = F.leaky_relu(self.conv2(x), negative_slope=0.1)
        return x


class up(nn.Module):

    def __init__(self, inChannels, outChannels):
        super(up, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, outChannels, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(2 * outChannels, outChannels, 3, stride=1,
            padding=1)

    def forward(self, x, skpCn):
        x = F.interpolate(x, scale_factor=2, mode='bilinear')
        x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
        x = F.leaky_relu(self.conv2(torch.cat((x, skpCn), 1)),
            negative_slope=0.1)
        return x


class UNetNew(nn.Module):

    def __init__(self, inChannels, outChannels):
        super(UNetNew, self).__init__()
        self.conv1 = nn.Conv2d(inChannels, 32, 7, stride=1, padding=3)
        self.conv2 = nn.Conv2d(32, 32, 7, stride=1, padding=3)
        self.down1 = down(32, 64, 5)
        self.down2 = down(64, 128, 3)
        self.down3 = down(128, 256, 3)
        self.down4 = down(256, 512, 3)
        self.down5 = down(512, 512, 3)
        self.up1 = up(512, 512)
        self.up2 = up(512, 256)
        self.up3 = up(256, 128)
        self.up4 = up(128, 64)
        self.up5 = up(64, 32)
        self.conv3 = nn.Conv2d(32, outChannels, 3, stride=1, padding=1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.down1.conv1.weight
        primals_7 = self.down1.conv1.bias
        primals_8 = self.down1.conv2.weight
        primals_9 = self.down1.conv2.bias
        primals_10 = self.down2.conv1.weight
        primals_11 = self.down2.conv1.bias
        primals_12 = self.down2.conv2.weight
        primals_13 = self.down2.conv2.bias
        primals_14 = self.down3.conv1.weight
        primals_15 = self.down3.conv1.bias
        primals_16 = self.down3.conv2.weight
        primals_17 = self.down3.conv2.bias
        primals_18 = self.down4.conv1.weight
        primals_19 = self.down4.conv1.bias
        primals_20 = self.down4.conv2.weight
        primals_21 = self.down4.conv2.bias
        primals_22 = self.down5.conv1.weight
        primals_23 = self.down5.conv1.bias
        primals_24 = self.down5.conv2.weight
        primals_25 = self.down5.conv2.bias
        primals_26 = self.up1.conv1.weight
        primals_27 = self.up1.conv1.bias
        primals_28 = self.up1.conv2.weight
        primals_29 = self.up1.conv2.bias
        primals_30 = self.up2.conv1.weight
        primals_31 = self.up2.conv1.bias
        primals_32 = self.up2.conv2.weight
        primals_33 = self.up2.conv2.bias
        primals_34 = self.up3.conv1.weight
        primals_35 = self.up3.conv1.bias
        primals_36 = self.up3.conv2.weight
        primals_37 = self.up3.conv2.bias
        primals_38 = self.up4.conv1.weight
        primals_39 = self.up4.conv1.bias
        primals_40 = self.up4.conv2.weight
        primals_41 = self.up4.conv2.bias
        primals_42 = self.up5.conv1.weight
        primals_43 = self.up5.conv1.bias
        primals_44 = self.up5.conv2.weight
        primals_45 = self.up5.conv2.bias
        primals_46 = self.conv3.weight
        primals_47 = self.conv3.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]
