# 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_4/inductor_cache/ej/cejfrwnzxinkchwn6symdb72fdtj7gix5hy2vuswodhbeh45mrae.py
# Topologically Sorted Source Nodes: [conv2d, xb], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   xb => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7z/c7zuih2ysjtir5rh5seep5ijnhokjlgkyjw2edhf257ahvz4iipr.py
# Topologically Sorted Source Nodes: [xb_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   xb_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xq/cxqz2dr7nh2qabrtemj52pazmhrknj5ltcy32ka252ia6a3jgpqi.py
# Topologically Sorted Source Nodes: [conv2d_2, xb_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   xb_3 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pr/cpri5daxkfbmt5ostbhb5o2avircr64a2rmdkxfackaxyjfc7owe.py
# Topologically Sorted Source Nodes: [xb_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   xb_5 => getitem_2, getitem_3
# Graph fragment:
#   %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/of/cof37d5wbqzvtkioj7k4me7wqpvfv55rs62ytonj7gij2o3abnod.py
# Topologically Sorted Source Nodes: [conv2d_4, xb_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   xb_6 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mn/cmnzsv2cdbsuq2sygridqvwumzmcvknuthlumel5m25l2ajsr4ft.py
# Topologically Sorted Source Nodes: [xb_9], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   xb_9 => getitem_4, getitem_5
# Graph fragment:
#   %getitem_4 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 0), kwargs = {})
#   %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + (2*x0) + (32*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ic/cicsjqc3cfcjzqlztx4hz7ssqwe47ngo3g2onc6463k3vgfmt5cw.py
# Topologically Sorted Source Nodes: [conv2d_7, xb_10], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_7 => convolution_7
#   xb_10 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_6 = async_compile.triton('triton_poi_fused_convolution_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 512
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fr/cfr2x7a5se3m5rc3f3kabe56eo5go4iqj4ir2h2x6uilzdlgxvdz.py
# Topologically Sorted Source Nodes: [xb_13], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   xb_13 => getitem_6, getitem_7
# Graph fragment:
#   %getitem_6 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 0), kwargs = {})
#   %getitem_7 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + (2*x0) + (16*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jo/cjoblr2qy3htqgyjuexilg6o7jcy5fuhn6ukmb2m7pl4wgyimhyr.py
# Topologically Sorted Source Nodes: [conv2d_10, xb_14], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_10
#   xb_14 => relu_10
# Graph fragment:
#   %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_22, %primals_23, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 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_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qo/cqobn7oxofqnkscngmddvrmbhytcjywexzhu5i3ly356tpx4e52m.py
# Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   xb_20 => 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_9 = async_compile.triton('triton_poi_fused__to_copy_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=[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_9', '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_9(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_4/inductor_cache/lg/clgwm4hks66cyouov5gzbhx2fy5stknbnxg76mwbi5anxv6uo5yd.py
# Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   xb_20 => 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, 3), kwargs = {})
triton_poi_fused_add_clamp_10 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_10', '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_10(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_4/inductor_cache/o4/co4bu545dlch25hmlo47qpovnew5tykld4sa6ajgxk5zpfagednr.py
# Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   xb_20 => add, clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul, sub, sub_2
# Graph fragment:
#   %iota : [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 : [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 : [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, 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_11 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_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=[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_11', '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_11(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_4/inductor_cache/jf/cjftsrxgtpu4sovd7efpxvwdvhhcnm7gd3vawiuk5x3jcobxgvlo.py
# Topologically Sorted Source Nodes: [conv2d_15, xb_19, xb_20], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   xb_19 => relu_15
#   xb_20 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_4, add_5, add_6, mul_2, mul_3, mul_4, sub_3, sub_4, sub_6
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_14, %primals_32, %primals_33, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_15 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_15,), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_15, [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 = (%relu_15, [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 = (%relu_15, [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 = (%relu_15, [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_2 : [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_2), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {})
#   %mul_4 : [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_4), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_12 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_12', 'mutated_arg_names': ['in_out_ptr0'], '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_mul_relu_sub_12(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (4*tmp4) + (16*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (4*tmp28) + (16*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (4*tmp28) + (16*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ih/cihx5oqvrkl4zc7e5qqmedsgxhuivhohnjp22ct7lkvoggqnqh6c.py
# Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   xb_24 => 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_13 = async_compile.triton('triton_poi_fused__to_copy_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=[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_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__to_copy_13(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_4/inductor_cache/hc/chcfndmqiv442mr7m6nveigwcvgszcejf2ewkage7wwvryhybbi7.py
# Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   xb_24 => 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, 7), kwargs = {})
triton_poi_fused_add_clamp_14 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_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_add_clamp_14(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_4/inductor_cache/l5/cl5v5bfaau643mrwldds5mdahdiyxyq724lydm4ymzjqvzayxf52.py
# Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   xb_24 => add_7, clamp_max_6, clamp_min_4, clamp_min_6, convert_element_type_4, iota_2, mul_5, sub_7, sub_9
# Graph fragment:
#   %iota_2 : [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_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_5 : [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_5, 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_15 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_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=[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_15', '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_15(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_4/inductor_cache/oq/coq4vp34gmio2aatgdzom3j3z6pxtuuazjcaeb4ls34h6wlbxpj6.py
# Topologically Sorted Source Nodes: [conv2d_18, xb_23, xb_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   xb_23 => relu_18
#   xb_24 => _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, add_11, add_12, add_13, mul_7, mul_8, mul_9, sub_10, sub_11, sub_13
# Graph fragment:
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_17, %primals_38, %primals_39, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_18 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_18,), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_18, [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 = (%relu_18, [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 = (%relu_18, [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 = (%relu_18, [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_7 : [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_7), kwargs = {})
#   %sub_11 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_7, %_unsafe_index_6), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_11, %clamp_max_6), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_6, %mul_8), kwargs = {})
#   %sub_13 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_12, %add_11), kwargs = {})
#   %mul_9 : [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_9), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16', 'mutated_arg_names': ['in_out_ptr0'], '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_mul_relu_sub_16(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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 // 16) % 16
    x0 = xindex % 16
    x6 = (xindex // 256)
    x2 = (xindex // 256) % 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')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (8*tmp28) + (64*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (8*tmp28) + (64*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5g/c5ggyr7jnhi23eg72gpwqr3hslygoiouihmb56fnnso5qfzem5ba.py
# Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   xb_28 => 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_17 = async_compile.triton('triton_poi_fused__to_copy_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=[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_17', '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_17(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_4/inductor_cache/hm/chmsacle376qyl75ntwkdgyb6v56s3y5uwppedjfaqghfg6lkoud.py
# Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   xb_28 => 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, 15), kwargs = {})
triton_poi_fused_add_clamp_18 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_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_add_clamp_18(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_4/inductor_cache/dl/cdlpe2253rion3yxg6w3p2mqmuc62kuuse4hozslkjvqgxzv5qtt.py
# Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   xb_28 => add_14, clamp_max_10, clamp_min_10, clamp_min_8, convert_element_type_8, iota_4, mul_10, sub_14, sub_16
# Graph fragment:
#   %iota_4 : [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_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_10 : [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_10, 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_19 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_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=[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_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__to_copy_add_arange_clamp_mul_sub_19(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_4/inductor_cache/7a/c7ax4dzfbh262ilngq67wcfmdvutcp7kudzakc5yuzmso3ympa66.py
# Topologically Sorted Source Nodes: [conv2d_21, xb_27, xb_28], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_21 => convolution_21
#   xb_27 => relu_21
#   xb_28 => _unsafe_index_10, _unsafe_index_11, _unsafe_index_8, _unsafe_index_9, add_18, add_19, add_20, mul_12, mul_13, mul_14, sub_17, sub_18, sub_20
# Graph fragment:
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_20, %primals_44, %primals_45, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_21 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_21,), kwargs = {})
#   %_unsafe_index_8 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_21, [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 = (%relu_21, [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 = (%relu_21, [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 = (%relu_21, [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_12 : [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_12), kwargs = {})
#   %sub_18 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_11, %_unsafe_index_10), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_18, %clamp_max_10), kwargs = {})
#   %add_19 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_10, %mul_13), kwargs = {})
#   %sub_20 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_19, %add_18), kwargs = {})
#   %mul_14 : [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_14), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_20 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_20', 'mutated_arg_names': ['in_out_ptr0'], '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_mul_relu_sub_20(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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 // 32) % 32
    x0 = xindex % 32
    x6 = (xindex // 1024)
    x2 = (xindex // 1024) % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (16*tmp4) + (256*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (16*tmp28) + (256*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (16*tmp28) + (256*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qv/cqvefa32x5rjj7irwtfogn4u2w6gvkwfxnwbuwb7eooewpfzf37q.py
# Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   xb_31 => 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_21 = async_compile.triton('triton_poi_fused__to_copy_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=[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_21', '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_21(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_4/inductor_cache/op/copwembemgzxhwx7uxgvxuougcxamzzez5wg5qhohirbeiz5oq45.py
# Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   xb_31 => 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, 31), kwargs = {})
triton_poi_fused_add_clamp_22 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_22', '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_22(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_4/inductor_cache/wl/cwlq336vatw6mc26rezgdnidsuwnnkp53jx4nkr5wucawx2gyw72.py
# Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
# Source node to ATen node mapping:
#   xb_31 => add_21, clamp_max_14, clamp_min_12, clamp_min_14, convert_element_type_12, iota_6, mul_15, sub_21, sub_23
# Graph fragment:
#   %iota_6 : [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_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_15 : [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_15, 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_23 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_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=[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_23', '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_23(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_4/inductor_cache/uw/cuwazjpdi2ud7vt6rpj6ead3rbw6ynz4nmoit3ltfb4b25how5n6.py
# Topologically Sorted Source Nodes: [conv2d_23, xb_30, xb_31], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_23 => convolution_23
#   xb_30 => relu_23
#   xb_31 => _unsafe_index_12, _unsafe_index_13, _unsafe_index_14, _unsafe_index_15, add_25, add_26, add_27, mul_17, mul_18, mul_19, sub_24, sub_25, sub_27
# Graph fragment:
#   %convolution_23 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_22, %primals_48, %primals_49, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_23 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_23,), kwargs = {})
#   %_unsafe_index_12 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_23, [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 = (%relu_23, [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 = (%relu_23, [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 = (%relu_23, [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_17 : [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_17), kwargs = {})
#   %sub_25 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_15, %_unsafe_index_14), kwargs = {})
#   %mul_18 : [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_18), kwargs = {})
#   %sub_27 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_26, %add_25), kwargs = {})
#   %mul_19 : [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_19), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_24 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_24', 'mutated_arg_names': ['in_out_ptr0'], '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_mul_relu_sub_24(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    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) % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (32*tmp4) + (1024*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (32*tmp28) + (1024*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (32*tmp28) + (1024*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/re/cre3ak6o2saect6amu6tsyjl7a4plwy5lguftow43vqoayjlgt7n.py
# Topologically Sorted Source Nodes: [xb_34], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   xb_34 => convolution_26
# Graph fragment:
#   %convolution_26 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_25, %primals_54, %primals_55, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_25 = async_compile.triton('triton_poi_fused_convolution_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_25', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_25(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tq/ctqhq6cy47vsdafizc6sxsrcfnxdxi2dhxjntttirzomersmnlkq.py
# Topologically Sorted Source Nodes: [conv2d_23, xb_30], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_23 => convolution_23
#   xb_30 => relu_23
# Graph fragment:
#   %convolution_23 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_22, %primals_48, %primals_49, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_23 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_23,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_23, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_26 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[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_relu_threshold_backward_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_26(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 // 1024) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ra/crarwbtmbrx3b4h64lqsjswgxfg6brdzgif5ojw35jn6rljxqw7a.py
# Topologically Sorted Source Nodes: [conv2d_21, xb_27], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_21 => convolution_21
#   xb_27 => relu_21
# Graph fragment:
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_20, %primals_44, %primals_45, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_21 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_21,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_21, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_27 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_27', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_27(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 // 256) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dj/cdjcwmbvu3okxehjk6pkr5lujyuiry66qxee45kpmgmqkkytjklt.py
# Topologically Sorted Source Nodes: [conv2d_18, xb_23], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   xb_23 => relu_18
# Graph fragment:
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_17, %primals_38, %primals_39, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_18 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_18,), kwargs = {})
#   %le_7 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_18, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_28 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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_relu_threshold_backward_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_relu_threshold_backward_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 // 64) % 512
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3s/c3smq45fyapekpgl655qfcu3ff35ynbwigvelvb6n64hodqfj66x.py
# Topologically Sorted Source Nodes: [conv2d_15, xb_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   xb_19 => relu_15
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_14, %primals_32, %primals_33, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_15 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_15,), kwargs = {})
#   %le_10 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_15, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_29 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[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_relu_threshold_backward_29', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_29(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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55 = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256, ), (1, ))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256, ), (1, ))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (512, ), (1, ))
    assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 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, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (512, ), (1, ))
    assert_size_stride(primals_30, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (512, ), (1, ))
    assert_size_stride(primals_32, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (512, ), (1, ))
    assert_size_stride(primals_34, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (512, ), (1, ))
    assert_size_stride(primals_36, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_37, (512, ), (1, ))
    assert_size_stride(primals_38, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_39, (512, ), (1, ))
    assert_size_stride(primals_40, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (256, ), (1, ))
    assert_size_stride(primals_42, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_43, (256, ), (1, ))
    assert_size_stride(primals_44, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (256, ), (1, ))
    assert_size_stride(primals_46, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (128, ), (1, ))
    assert_size_stride(primals_48, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_49, (128, ), (1, ))
    assert_size_stride(primals_50, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (64, ), (1, ))
    assert_size_stride(primals_52, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_53, (64, ), (1, ))
    assert_size_stride(primals_54, (1, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_55, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, xb], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 1048576, grid=grid(1048576), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, xb_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 1048576, grid=grid(1048576), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [xb_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 262144, grid=grid(262144), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, xb_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf7, primals_7, 524288, grid=grid(524288), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, xb_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf9, primals_9, 524288, grid=grid(524288), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [xb_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf9, buf10, buf11, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, xb_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf13, primals_11, 262144, grid=grid(262144), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, xb_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf15, primals_13, 262144, grid=grid(262144), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, xb_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf17, primals_15, 262144, grid=grid(262144), stream=stream0)
        del primals_15
        buf18 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.float32)
        buf19 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.int8)
        # Topologically Sorted Source Nodes: [xb_9], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf17, buf18, buf19, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, xb_10], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf21, primals_17, 131072, grid=grid(131072), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, xb_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf23, primals_19, 131072, grid=grid(131072), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, xb_12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf25, primals_21, 131072, grid=grid(131072), stream=stream0)
        del primals_21
        buf26 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        buf27 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.int8)
        # Topologically Sorted Source Nodes: [xb_13], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf25, buf26, buf27, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf26, primals_22, 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 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10, xb_14], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf29, primals_23, 32768, grid=grid(32768), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, xb_15], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf31, primals_25, 32768, grid=grid(32768), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12, xb_16], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf33, primals_27, 32768, grid=grid(32768), stream=stream0)
        del primals_27
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, xb_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf35, primals_29, 32768, grid=grid(32768), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_14, xb_18], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf37, primals_31, 32768, grid=grid(32768), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_32, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf39 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_9.run(buf39, 8, grid=grid(8), stream=stream0)
        buf40 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_10.run(buf40, 8, grid=grid(8), stream=stream0)
        buf41 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_9.run(buf41, 8, grid=grid(8), stream=stream0)
        buf42 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_10.run(buf42, 8, grid=grid(8), stream=stream0)
        buf43 = empty_strided_cuda((8, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [xb_20], 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_11.run(buf43, 8, grid=grid(8), stream=stream0)
        buf45 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xb_20], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_11.run(buf45, 8, grid=grid(8), stream=stream0)
        buf46 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.float32)
        buf47 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [conv2d_15, xb_19, xb_20], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_12.run(buf47, buf39, buf41, buf38, primals_33, buf42, buf43, buf40, buf45, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16, xb_21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf49, primals_35, 131072, grid=grid(131072), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf50 = extern_kernels.convolution(buf49, primals_36, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf51 = buf50; del buf50  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17, xb_22], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf51, primals_37, 131072, grid=grid(131072), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf52 = extern_kernels.convolution(buf51, primals_38, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf53 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_13.run(buf53, 16, grid=grid(16), stream=stream0)
        buf54 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_14.run(buf54, 16, grid=grid(16), stream=stream0)
        buf55 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_13.run(buf55, 16, grid=grid(16), stream=stream0)
        buf56 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_14.run(buf56, 16, grid=grid(16), stream=stream0)
        buf57 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [xb_24], 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_15.run(buf57, 16, grid=grid(16), stream=stream0)
        buf59 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xb_24], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_15.run(buf59, 16, grid=grid(16), stream=stream0)
        buf60 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1), torch.float32)
        buf61 = buf60; del buf60  # reuse
        # Topologically Sorted Source Nodes: [conv2d_18, xb_23, xb_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16.run(buf61, buf53, buf55, buf52, primals_39, buf56, buf57, buf54, buf59, 524288, grid=grid(524288), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_19], Original ATen: [aten.convolution]
        buf62 = extern_kernels.convolution(buf61, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf63 = buf62; del buf62  # reuse
        # Topologically Sorted Source Nodes: [conv2d_19, xb_25], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf63, primals_41, 262144, grid=grid(262144), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf63, primals_42, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf65 = buf64; del buf64  # reuse
        # Topologically Sorted Source Nodes: [conv2d_20, xb_26], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf65, primals_43, 262144, grid=grid(262144), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, primals_44, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf67 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_17.run(buf67, 32, grid=grid(32), stream=stream0)
        buf68 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_18.run(buf68, 32, grid=grid(32), stream=stream0)
        buf69 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_17.run(buf69, 32, grid=grid(32), stream=stream0)
        buf70 = empty_strided_cuda((32, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_18.run(buf70, 32, grid=grid(32), stream=stream0)
        buf71 = empty_strided_cuda((32, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [xb_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_19.run(buf71, 32, grid=grid(32), stream=stream0)
        buf73 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xb_28], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_19.run(buf73, 32, grid=grid(32), stream=stream0)
        buf74 = empty_strided_cuda((4, 256, 32, 32), (262144, 1024, 32, 1), torch.float32)
        buf75 = buf74; del buf74  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21, xb_27, xb_28], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_20.run(buf75, buf67, buf69, buf66, primals_45, buf70, buf71, buf68, buf73, 1048576, grid=grid(1048576), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf76 = extern_kernels.convolution(buf75, primals_46, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf76, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf77 = buf76; del buf76  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22, xb_29], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf77, primals_47, 524288, grid=grid(524288), stream=stream0)
        del primals_47
        # Topologically Sorted Source Nodes: [conv2d_23], Original ATen: [aten.convolution]
        buf78 = extern_kernels.convolution(buf77, primals_48, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf79 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_21.run(buf79, 64, grid=grid(64), stream=stream0)
        buf80 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_22.run(buf80, 64, grid=grid(64), stream=stream0)
        buf81 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp]
        triton_poi_fused__to_copy_21.run(buf81, 64, grid=grid(64), stream=stream0)
        buf82 = empty_strided_cuda((64, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_22.run(buf82, 64, grid=grid(64), stream=stream0)
        buf83 = empty_strided_cuda((64, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [xb_31], 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_23.run(buf83, 64, grid=grid(64), stream=stream0)
        buf85 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xb_31], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_23.run(buf85, 64, grid=grid(64), stream=stream0)
        buf86 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1), torch.float32)
        buf87 = buf86; del buf86  # reuse
        # Topologically Sorted Source Nodes: [conv2d_23, xb_30, xb_31], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_24.run(buf87, buf79, buf81, buf78, primals_49, buf82, buf83, buf80, buf85, 2097152, grid=grid(2097152), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        buf88 = extern_kernels.convolution(buf87, primals_50, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf88, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf89 = buf88; del buf88  # reuse
        # Topologically Sorted Source Nodes: [conv2d_24, xb_32], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf89, primals_51, 1048576, grid=grid(1048576), stream=stream0)
        del primals_51
        # Topologically Sorted Source Nodes: [conv2d_25], Original ATen: [aten.convolution]
        buf90 = extern_kernels.convolution(buf89, primals_52, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf90, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf91 = buf90; del buf90  # reuse
        # Topologically Sorted Source Nodes: [conv2d_25, xb_33], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf91, primals_53, 1048576, grid=grid(1048576), stream=stream0)
        del primals_53
        # Topologically Sorted Source Nodes: [xb_34], Original ATen: [aten.convolution]
        buf92 = extern_kernels.convolution(buf91, primals_54, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf93 = buf92; del buf92  # reuse
        # Topologically Sorted Source Nodes: [xb_34], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_25.run(buf93, primals_55, 16384, grid=grid(16384), stream=stream0)
        del primals_55
        buf94 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_23, xb_30], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_26.run(buf78, primals_49, buf94, 524288, grid=grid(524288), stream=stream0)
        del buf78
        del primals_49
        buf95 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_21, xb_27], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_27.run(buf66, primals_45, buf95, 262144, grid=grid(262144), stream=stream0)
        del buf66
        del primals_45
        buf96 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_18, xb_23], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_28.run(buf52, primals_39, buf96, 131072, grid=grid(131072), stream=stream0)
        del buf52
        del primals_39
        buf97 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_15, xb_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_29.run(buf38, primals_33, buf97, 32768, grid=grid(32768), stream=stream0)
        del buf38
        del primals_33
    return (buf93, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, primals_32, primals_34, primals_36, primals_38, primals_40, primals_42, primals_44, primals_46, primals_48, primals_50, primals_52, primals_54, buf1, buf3, buf4, buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf17, buf18, buf19, buf21, buf23, buf25, buf26, buf27, buf29, buf31, buf33, buf35, buf37, buf39, buf40, buf41, buf42, buf43, buf45, buf47, buf49, buf51, buf53, buf54, buf55, buf56, buf57, buf59, buf61, buf63, buf65, buf67, buf68, buf69, buf70, buf71, buf73, buf75, buf77, buf79, buf80, buf81, buf82, buf83, buf85, buf87, buf89, buf91, buf94, buf95, buf96, buf97, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 256, 3, 3), (2304, 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((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((512, 512, 3, 3), (4608, 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, 512, 3, 3), (4608, 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((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((1, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55])
    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 Decoder(nn.Module):

    def __init__(self):
        super(Decoder, self).__init__()
        self.conv6_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv6_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv6_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.upsample1 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv7_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv7_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv7_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv8_1 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)
        self.conv8_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv8_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.upsample3 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv9_1 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.conv9_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.upsample4 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv10_1 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1)
        self.conv10_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv10_3 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)

    def forward(self, xb):
        xb = F.relu(self.conv6_1(xb))
        xb = F.relu(self.conv6_2(xb))
        xb = F.relu(self.conv6_3(xb))
        xb = self.upsample1(xb)
        xb = F.relu(self.conv7_1(xb))
        xb = F.relu(self.conv7_2(xb))
        xb = F.relu(self.conv7_3(xb))
        xb = self.upsample2(xb)
        xb = F.relu(self.conv8_1(xb))
        xb = F.relu(self.conv8_2(xb))
        xb = F.relu(self.conv8_3(xb))
        xb = self.upsample3(xb)
        xb = F.relu(self.conv9_1(xb))
        xb = F.relu(self.conv9_2(xb))
        xb = self.upsample4(xb)
        xb = F.relu(self.conv10_1(xb))
        xb = F.relu(self.conv10_2(xb))
        xb = self.conv10_3(xb)
        return xb


class Encoder(nn.Module):

    def __init__(self, model_dict=None):
        super(Encoder, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(2, stride=2)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(2, stride=2)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.pool3 = nn.MaxPool2d(2, stride=2)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.pool4 = nn.MaxPool2d(2, stride=2)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        if model_dict is not None:
            self.conv1_1.weight.data = model_dict['conv1_1_weight']
            self.conv1_1.bias.data = model_dict['conv1_1_bias']
            self.conv1_2.weight.data = model_dict['conv1_2_weight']
            self.conv1_2.bias.data = model_dict['conv1_2_bias']
            self.conv2_1.weight.data = model_dict['conv2_1_weight']
            self.conv2_1.bias.data = model_dict['conv2_1_bias']
            self.conv2_2.weight.data = model_dict['conv2_2_weight']
            self.conv2_2.bias.data = model_dict['conv2_2_bias']
            self.conv3_1.weight.data = model_dict['conv3_1_weight']
            self.conv3_1.bias.data = model_dict['conv3_1_bias']
            self.conv3_2.weight.data = model_dict['conv3_2_weight']
            self.conv3_2.bias.data = model_dict['conv3_2_bias']
            self.conv3_3.weight.data = model_dict['conv3_3_weight']
            self.conv3_3.bias.data = model_dict['conv3_3_bias']
            self.conv4_1.weight.data = model_dict['conv4_1_weight']
            self.conv4_1.bias.data = model_dict['conv4_1_bias']
            self.conv4_2.weight.data = model_dict['conv4_2_weight']
            self.conv4_2.bias.data = model_dict['conv4_2_bias']
            self.conv4_3.weight.data = model_dict['conv4_3_weight']
            self.conv4_3.bias.data = model_dict['conv4_3_bias']
            self.conv5_1.weight.data = model_dict['conv5_1_weight']
            self.conv5_1.bias.data = model_dict['conv5_1_bias']
            self.conv5_2.weight.data = model_dict['conv5_2_weight']
            self.conv5_2.bias.data = model_dict['conv5_2_bias']
            self.conv5_3.weight.data = model_dict['conv5_3_weight']
            self.conv5_3.bias.data = model_dict['conv5_3_bias']

    def forward(self, xb):
        xb = F.relu(self.conv1_1(xb))
        xb = F.relu(self.conv1_2(xb))
        xb = self.pool1(xb)
        xb = F.relu(self.conv2_1(xb))
        xb = F.relu(self.conv2_2(xb))
        xb = self.pool2(xb)
        xb = F.relu(self.conv3_1(xb))
        xb = F.relu(self.conv3_2(xb))
        xb = F.relu(self.conv3_3(xb))
        xb = self.pool3(xb)
        xb = F.relu(self.conv4_1(xb))
        xb = F.relu(self.conv4_2(xb))
        xb = F.relu(self.conv4_3(xb))
        xb = self.pool4(xb)
        xb = F.relu(self.conv5_1(xb))
        xb = F.relu(self.conv5_2(xb))
        xb = F.relu(self.conv5_3(xb))
        return xb


class Generator(nn.Module):

    def __init__(self, model_dict=None):
        super(Generator, self).__init__()
        self.encoder = Encoder(model_dict)
        self.decoder = Decoder()

    def forward(self, xb):
        xb = self.encoder(xb)
        xb = self.decoder(xb)
        return xb


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


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

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


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (17 + 2 * x0 + 32 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), None, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_9(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_10(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_11(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_mul_relu_sub_12(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 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')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 4 * tmp4 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 4 * tmp28 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 4 * tmp28 + 16 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused__to_copy_13(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_14(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_15(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_mul_relu_sub_16(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x6 = xindex // 256
    x2 = xindex // 256 % 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')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + 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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 8 * tmp28 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 8 * tmp28 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused__to_copy_17(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_18(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_19(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_mul_relu_sub_20(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 32 % 32
    x0 = xindex % 32
    x6 = xindex // 1024
    x2 = xindex // 1024 % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + 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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 16 * tmp4 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 16 * tmp28 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 16 * tmp28 + 256 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused__to_copy_21(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_22(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_23(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_mul_relu_sub_24(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 64
    x0 = xindex % 64
    x6 = xindex // 4096
    x2 = xindex // 4096 % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + 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')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 32 * tmp4 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 32 * tmp28 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 32 * tmp28 + 1024 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_convolution_25(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_26(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 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_27(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 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_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 // 64 % 512
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_29(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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 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, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (512,), (1,))
    assert_size_stride(primals_30, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (512,), (1,))
    assert_size_stride(primals_32, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_33, (512,), (1,))
    assert_size_stride(primals_34, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (512,), (1,))
    assert_size_stride(primals_36, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_37, (512,), (1,))
    assert_size_stride(primals_38, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_39, (512,), (1,))
    assert_size_stride(primals_40, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (256,), (1,))
    assert_size_stride(primals_42, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_43, (256,), (1,))
    assert_size_stride(primals_44, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_45, (256,), (1,))
    assert_size_stride(primals_46, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_47, (128,), (1,))
    assert_size_stride(primals_48, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_49, (128,), (1,))
    assert_size_stride(primals_50, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (64,), (1,))
    assert_size_stride(primals_52, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_53, (64,), (1,))
    assert_size_stride(primals_54, (1, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_55, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf1, primals_2,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf3, primals_5,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(262144)](buf3, buf4,
            buf5, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_2[grid(524288)](buf7, primals_7, 
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_2[grid(524288)](buf9, primals_9, 
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(131072)](buf9,
            buf10, buf11, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_4[grid(262144)](buf13, primals_11,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_4[grid(262144)](buf15, primals_13,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_13
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_4[grid(262144)](buf17, primals_15,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_15
        buf18 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .float32)
        buf19 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_5[grid(65536)](buf17,
            buf18, buf19, 65536, XBLOCK=256, num_warps=4, num_stages=1)
        buf20 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_6[grid(131072)](buf21, primals_17,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_17
        buf22 = extern_kernels.convolution(buf21, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf23 = buf22
        del buf22
        triton_poi_fused_convolution_relu_6[grid(131072)](buf23, primals_19,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_19
        buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_6[grid(131072)](buf25, primals_21,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_21
        buf26 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        buf27 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.int8
            )
        triton_poi_fused_max_pool2d_with_indices_7[grid(32768)](buf25,
            buf26, buf27, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        buf28 = extern_kernels.convolution(buf26, primals_22, 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 = buf28
        del buf28
        triton_poi_fused_convolution_relu_8[grid(32768)](buf29, primals_23,
            32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_23
        buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_relu_8[grid(32768)](buf31, primals_25,
            32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_25
        buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_relu_8[grid(32768)](buf33, primals_27,
            32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_27
        buf34 = extern_kernels.convolution(buf33, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_relu_8[grid(32768)](buf35, primals_29,
            32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_29
        buf36 = extern_kernels.convolution(buf35, primals_30, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_8[grid(32768)](buf37, primals_31,
            32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_31
        buf38 = extern_kernels.convolution(buf37, primals_32, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf39 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_9[grid(8)](buf39, 8, XBLOCK=8, num_warps=
            1, num_stages=1)
        buf40 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_10[grid(8)](buf40, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf41 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_9[grid(8)](buf41, 8, XBLOCK=8, num_warps=
            1, num_stages=1)
        buf42 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused_add_clamp_10[grid(8)](buf42, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf43 = empty_strided_cuda((8,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_11[grid(8)](buf43,
            8, XBLOCK=8, num_warps=1, num_stages=1)
        buf45 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_11[grid(8)](buf45,
            8, XBLOCK=8, num_warps=1, num_stages=1)
        buf46 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
            .float32)
        buf47 = buf46
        del buf46
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_12[grid
            (131072)](buf47, buf39, buf41, buf38, primals_33, buf42, buf43,
            buf40, buf45, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        buf48 = extern_kernels.convolution(buf47, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf49 = buf48
        del buf48
        triton_poi_fused_convolution_relu_6[grid(131072)](buf49, primals_35,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_35
        buf50 = extern_kernels.convolution(buf49, primals_36, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf51 = buf50
        del buf50
        triton_poi_fused_convolution_relu_6[grid(131072)](buf51, primals_37,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_37
        buf52 = extern_kernels.convolution(buf51, primals_38, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 512, 8, 8), (32768, 64, 8, 1))
        buf53 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_13[grid(16)](buf53, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf54 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_14[grid(16)](buf54, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf55 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_13[grid(16)](buf55, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf56 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_add_clamp_14[grid(16)](buf56, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf57 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_15[grid(16)](buf57,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf59 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_15[grid(16)](buf59,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf60 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1),
            torch.float32)
        buf61 = buf60
        del buf60
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16[grid
            (524288)](buf61, buf53, buf55, buf52, primals_39, buf56, buf57,
            buf54, buf59, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        buf62 = extern_kernels.convolution(buf61, primals_40, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf62, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf63 = buf62
        del buf62
        triton_poi_fused_convolution_relu_4[grid(262144)](buf63, primals_41,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_41
        buf64 = extern_kernels.convolution(buf63, primals_42, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf65 = buf64
        del buf64
        triton_poi_fused_convolution_relu_4[grid(262144)](buf65, primals_43,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_43
        buf66 = extern_kernels.convolution(buf65, primals_44, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 256, 16, 16), (65536, 256, 16, 1))
        buf67 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_17[grid(32)](buf67, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf68 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_18[grid(32)](buf68, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf69 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_17[grid(32)](buf69, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf70 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused_add_clamp_18[grid(32)](buf70, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf71 = empty_strided_cuda((32,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_19[grid(32)](buf71,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf73 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_19[grid(32)](buf73,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf74 = empty_strided_cuda((4, 256, 32, 32), (262144, 1024, 32, 1),
            torch.float32)
        buf75 = buf74
        del buf74
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_20[grid
            (1048576)](buf75, buf67, buf69, buf66, primals_45, buf70, buf71,
            buf68, buf73, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        buf76 = extern_kernels.convolution(buf75, primals_46, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf76, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf77 = buf76
        del buf76
        triton_poi_fused_convolution_relu_2[grid(524288)](buf77, primals_47,
            524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_47
        buf78 = extern_kernels.convolution(buf77, primals_48, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf79 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_21[grid(64)](buf79, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf80 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_22[grid(64)](buf80, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf81 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused__to_copy_21[grid(64)](buf81, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf82 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused_add_clamp_22[grid(64)](buf82, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf83 = empty_strided_cuda((64,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_23[grid(64)](buf83,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf85 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_23[grid(64)](buf85,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf86 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        buf87 = buf86
        del buf86
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_24[grid
            (2097152)](buf87, buf79, buf81, buf78, primals_49, buf82, buf83,
            buf80, buf85, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        buf88 = extern_kernels.convolution(buf87, primals_50, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf88, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf89 = buf88
        del buf88
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf89,
            primals_51, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_51
        buf90 = extern_kernels.convolution(buf89, primals_52, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf90, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf91 = buf90
        del buf90
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf91,
            primals_53, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_53
        buf92 = extern_kernels.convolution(buf91, primals_54, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf93 = buf92
        del buf92
        triton_poi_fused_convolution_25[grid(16384)](buf93, primals_55, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_55
        buf94 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_26[grid(524288)](
            buf78, primals_49, buf94, 524288, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf78
        del primals_49
        buf95 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_27[grid(262144)](
            buf66, primals_45, buf95, 262144, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf66
        del primals_45
        buf96 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
            .bool)
        triton_poi_fused_convolution_relu_threshold_backward_28[grid(131072)](
            buf52, primals_39, buf96, 131072, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf52
        del primals_39
        buf97 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_29[grid(32768)](
            buf38, primals_33, buf97, 32768, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf38
        del primals_33
    return (buf93, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, primals_22, primals_24, primals_26, primals_28,
        primals_30, primals_32, primals_34, primals_36, primals_38,
        primals_40, primals_42, primals_44, primals_46, primals_48,
        primals_50, primals_52, primals_54, buf1, buf3, buf4, buf5, buf7,
        buf9, buf10, buf11, buf13, buf15, buf17, buf18, buf19, buf21, buf23,
        buf25, buf26, buf27, buf29, buf31, buf33, buf35, buf37, buf39,
        buf40, buf41, buf42, buf43, buf45, buf47, buf49, buf51, buf53,
        buf54, buf55, buf56, buf57, buf59, buf61, buf63, buf65, buf67,
        buf68, buf69, buf70, buf71, buf73, buf75, buf77, buf79, buf80,
        buf81, buf82, buf83, buf85, buf87, buf89, buf91, buf94, buf95,
        buf96, buf97)


class Decoder(nn.Module):

    def __init__(self):
        super(Decoder, self).__init__()
        self.conv6_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv6_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv6_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.upsample1 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv7_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv7_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv7_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv8_1 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)
        self.conv8_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv8_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.upsample3 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv9_1 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.conv9_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.upsample4 = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=False)
        self.conv10_1 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1)
        self.conv10_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.conv10_3 = nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)

    def forward(self, xb):
        xb = F.relu(self.conv6_1(xb))
        xb = F.relu(self.conv6_2(xb))
        xb = F.relu(self.conv6_3(xb))
        xb = self.upsample1(xb)
        xb = F.relu(self.conv7_1(xb))
        xb = F.relu(self.conv7_2(xb))
        xb = F.relu(self.conv7_3(xb))
        xb = self.upsample2(xb)
        xb = F.relu(self.conv8_1(xb))
        xb = F.relu(self.conv8_2(xb))
        xb = F.relu(self.conv8_3(xb))
        xb = self.upsample3(xb)
        xb = F.relu(self.conv9_1(xb))
        xb = F.relu(self.conv9_2(xb))
        xb = self.upsample4(xb)
        xb = F.relu(self.conv10_1(xb))
        xb = F.relu(self.conv10_2(xb))
        xb = self.conv10_3(xb)
        return xb


class Encoder(nn.Module):

    def __init__(self, model_dict=None):
        super(Encoder, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(2, stride=2)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(2, stride=2)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.pool3 = nn.MaxPool2d(2, stride=2)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.pool4 = nn.MaxPool2d(2, stride=2)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        if model_dict is not None:
            self.conv1_1.weight.data = model_dict['conv1_1_weight']
            self.conv1_1.bias.data = model_dict['conv1_1_bias']
            self.conv1_2.weight.data = model_dict['conv1_2_weight']
            self.conv1_2.bias.data = model_dict['conv1_2_bias']
            self.conv2_1.weight.data = model_dict['conv2_1_weight']
            self.conv2_1.bias.data = model_dict['conv2_1_bias']
            self.conv2_2.weight.data = model_dict['conv2_2_weight']
            self.conv2_2.bias.data = model_dict['conv2_2_bias']
            self.conv3_1.weight.data = model_dict['conv3_1_weight']
            self.conv3_1.bias.data = model_dict['conv3_1_bias']
            self.conv3_2.weight.data = model_dict['conv3_2_weight']
            self.conv3_2.bias.data = model_dict['conv3_2_bias']
            self.conv3_3.weight.data = model_dict['conv3_3_weight']
            self.conv3_3.bias.data = model_dict['conv3_3_bias']
            self.conv4_1.weight.data = model_dict['conv4_1_weight']
            self.conv4_1.bias.data = model_dict['conv4_1_bias']
            self.conv4_2.weight.data = model_dict['conv4_2_weight']
            self.conv4_2.bias.data = model_dict['conv4_2_bias']
            self.conv4_3.weight.data = model_dict['conv4_3_weight']
            self.conv4_3.bias.data = model_dict['conv4_3_bias']
            self.conv5_1.weight.data = model_dict['conv5_1_weight']
            self.conv5_1.bias.data = model_dict['conv5_1_bias']
            self.conv5_2.weight.data = model_dict['conv5_2_weight']
            self.conv5_2.bias.data = model_dict['conv5_2_bias']
            self.conv5_3.weight.data = model_dict['conv5_3_weight']
            self.conv5_3.bias.data = model_dict['conv5_3_bias']

    def forward(self, xb):
        xb = F.relu(self.conv1_1(xb))
        xb = F.relu(self.conv1_2(xb))
        xb = self.pool1(xb)
        xb = F.relu(self.conv2_1(xb))
        xb = F.relu(self.conv2_2(xb))
        xb = self.pool2(xb)
        xb = F.relu(self.conv3_1(xb))
        xb = F.relu(self.conv3_2(xb))
        xb = F.relu(self.conv3_3(xb))
        xb = self.pool3(xb)
        xb = F.relu(self.conv4_1(xb))
        xb = F.relu(self.conv4_2(xb))
        xb = F.relu(self.conv4_3(xb))
        xb = self.pool4(xb)
        xb = F.relu(self.conv5_1(xb))
        xb = F.relu(self.conv5_2(xb))
        xb = F.relu(self.conv5_3(xb))
        return xb


class GeneratorNew(nn.Module):

    def __init__(self, model_dict=None):
        super(GeneratorNew, self).__init__()
        self.encoder = Encoder(model_dict)
        self.decoder = Decoder()

    def forward(self, input_0):
        primals_1 = self.encoder.conv1_1.weight
        primals_2 = self.encoder.conv1_1.bias
        primals_4 = self.encoder.conv1_2.weight
        primals_5 = self.encoder.conv1_2.bias
        primals_6 = self.encoder.conv2_1.weight
        primals_7 = self.encoder.conv2_1.bias
        primals_8 = self.encoder.conv2_2.weight
        primals_9 = self.encoder.conv2_2.bias
        primals_10 = self.encoder.conv3_1.weight
        primals_11 = self.encoder.conv3_1.bias
        primals_12 = self.encoder.conv3_2.weight
        primals_13 = self.encoder.conv3_2.bias
        primals_14 = self.encoder.conv3_3.weight
        primals_15 = self.encoder.conv3_3.bias
        primals_16 = self.encoder.conv4_1.weight
        primals_17 = self.encoder.conv4_1.bias
        primals_18 = self.encoder.conv4_2.weight
        primals_19 = self.encoder.conv4_2.bias
        primals_20 = self.encoder.conv4_3.weight
        primals_21 = self.encoder.conv4_3.bias
        primals_22 = self.encoder.conv5_1.weight
        primals_23 = self.encoder.conv5_1.bias
        primals_24 = self.encoder.conv5_2.weight
        primals_25 = self.encoder.conv5_2.bias
        primals_26 = self.encoder.conv5_3.weight
        primals_27 = self.encoder.conv5_3.bias
        primals_28 = self.decoder.conv6_1.weight
        primals_29 = self.decoder.conv6_1.bias
        primals_30 = self.decoder.conv6_2.weight
        primals_31 = self.decoder.conv6_2.bias
        primals_32 = self.decoder.conv6_3.weight
        primals_33 = self.decoder.conv6_3.bias
        primals_34 = self.decoder.conv7_1.weight
        primals_35 = self.decoder.conv7_1.bias
        primals_36 = self.decoder.conv7_2.weight
        primals_37 = self.decoder.conv7_2.bias
        primals_38 = self.decoder.conv7_3.weight
        primals_39 = self.decoder.conv7_3.bias
        primals_40 = self.decoder.conv8_1.weight
        primals_41 = self.decoder.conv8_1.bias
        primals_42 = self.decoder.conv8_2.weight
        primals_43 = self.decoder.conv8_2.bias
        primals_44 = self.decoder.conv8_3.weight
        primals_45 = self.decoder.conv8_3.bias
        primals_46 = self.decoder.conv9_1.weight
        primals_47 = self.decoder.conv9_1.bias
        primals_48 = self.decoder.conv9_2.weight
        primals_49 = self.decoder.conv9_2.bias
        primals_50 = self.decoder.conv10_1.weight
        primals_51 = self.decoder.conv10_1.bias
        primals_52 = self.decoder.conv10_2.weight
        primals_53 = self.decoder.conv10_2.bias
        primals_54 = self.decoder.conv10_3.weight
        primals_55 = self.decoder.conv10_3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55])
        return output[0]
