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

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


# kernel path: runs/run_shard_8/inductor_cache/oi/coi4geilgwlqjngeai6hg4iruvpzcrvvpmho77vuyz3ppbade2sa.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [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=[16777216], 
    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 = 16516096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 64516) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/7o/c7ohemjzrjpmo5tghcbldpg3s3xexqjehsqi5j3see7mnjgihu4y.py
# Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_2 => convolution_1
#   x_3 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[16777216], 
    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_1', '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16257024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 63504) % 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_8/inductor_cache/xi/cxil7wfmjnrkoslosnjnwmoqebyussbsklzthwtzwxz2o74l47gs.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => 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_2 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[4194304], 
    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_2', '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_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4064256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 126
    x3 = (xindex // 126)
    x2 = (xindex // 15876)
    x4 = xindex % 15876
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (504*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (504*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (252 + (2*x0) + (504*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (253 + (2*x0) + (504*x3)), xmask, 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 + (x4 + (15904*x2)), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + (16000*x2)), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/mo/cmozurfjru5huadwzrh5yjoitgoxelkjngdyjsgtehbyvs24isqk.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_4 => convolution_2
#   x_5 => 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], [0, 0], [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_3 = async_compile.triton('triton_poi_fused_convolution_relu_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=[8388608], 
    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_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7872512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 15376) % 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_8/inductor_cache/7z/c7zdlcnndhik4fhrd3ma2afaitw5ju2jqj5dhl26ipgwz2hgiher.py
# Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_6 => convolution_3
#   x_7 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), 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=[8388608], 
    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 = 7620608
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 14884) % 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_8/inductor_cache/bb/cbbeqfykl2wgg3c65cet5cy67zimq7orfpepzs3vr2rlhu74t6er.py
# Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_1 => 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_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=[2097152], 
    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 = 1905152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x3 = (xindex // 61)
    x2 = (xindex // 3721)
    x4 = xindex % 3721
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (244*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (244*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (122 + (2*x0) + (244*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (123 + (2*x0) + (244*x3)), xmask, 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 + (x4 + (3744*x2)), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + (3840*x2)), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/al/cale6iof6zxwoy6xmrfdcnforqmwzzvzi3cb4g6s4jjxqw3hb7np.py
# Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_8 => convolution_4
#   x_9 => 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], [0, 0], [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_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=[4194304], 
    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 = 3564544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3481) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/o2/co2av5pccpjevh6yky7hhdrsglymtzut2q3artudzrezlirfrzcg.py
# Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_10 => convolution_5
#   x_11 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
triton_poi_fused_convolution_relu_7 = async_compile.triton('triton_poi_fused_convolution_relu_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=[4194304], 
    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_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3326976
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3249) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kt/cktc65bbrriywtquenbdrltvs6witq3gtcn3rvhoj2hholh5ihhi.py
# Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_2 => 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_8 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[1048576], 
    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_8', '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_8(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 802816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 28
    x1 = (xindex // 28) % 28
    x2 = (xindex // 784)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (114*x1) + (3249*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (114*x1) + (3249*x2)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (57 + (2*x0) + (114*x1) + (3249*x2)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (58 + (2*x0) + (114*x1) + (3249*x2)), 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 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/hi/chiqbenmv4ahnhr5zzytacsmj2bam534skwucufjcoevmj65liid.py
# Topologically Sorted Source Nodes: [x_12, x_13], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_12 => convolution_6
#   x_13 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
triton_poi_fused_convolution_relu_9 = async_compile.triton('triton_poi_fused_convolution_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1384448
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 676) % 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_8/inductor_cache/4n/c4ng7b3xlqdmz4wf6padqdaqd63cmlob5sujzf4en6rjcglhiane.py
# Topologically Sorted Source Nodes: [x_14, x_15], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_14 => convolution_7
#   x_15 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_6, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_10 = async_compile.triton('triton_poi_fused_convolution_relu_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2097152], 
    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_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1179648
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 576) % 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_8/inductor_cache/35/c356cd7zgpicezkybpa4roi4bdkpka2o2mqfytmksv4niqhdjpg5.py
# Topologically Sorted Source Nodes: [max_pool2d_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_3 => 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_11 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_11', '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_11(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 294912
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 12
    x1 = (xindex // 12)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (48*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (48*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (24 + (2*x0) + (48*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (25 + (2*x0) + (48*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_8/inductor_cache/h4/ch4ssn455dn4jzxavkj54g5nkxgb7fualnfv6kxozp5tbwrysclf.py
# Topologically Sorted Source Nodes: [x_16, x_17], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_16 => convolution_8
#   x_17 => relu_8
# Graph fragment:
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
triton_poi_fused_convolution_relu_12 = async_compile.triton('triton_poi_fused_convolution_relu_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 409600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 100) % 1024
    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_8/inductor_cache/ym/cymeve4n2tfhbdp57kl6vbua7sg3rraw7fbbupi7awglsnzhjnth.py
# Topologically Sorted Source Nodes: [x_18, x_19], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_18 => convolution_9
#   x_19 => relu_9
# Graph fragment:
#   %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_8, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
triton_poi_fused_convolution_relu_13 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_13(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 // 64) % 1024
    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_8/inductor_cache/g2/cg2m4cjaoijmslxcqpdriumxex3jthbggayhwvzgr2fp4jr2xoct.py
# Topologically Sorted Source Nodes: [up_1], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_1 => 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_14 = async_compile.triton('triton_poi_fused__to_copy_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__to_copy_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kj/ckjtbjd57qolhrmtahgjvgra32ws3y6k2py4qtzmi63emr64mpos.py
# Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_1 => add, clamp_max
# Graph fragment:
#   %add : [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, 7), kwargs = {})
triton_poi_fused_add_clamp_15 = async_compile.triton('triton_poi_fused_add_clamp_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: '*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_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_add_clamp_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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 7, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/bw/cbwtnrcuwwmine5iorv4m5uhjwildqcnmybfugynwzc3tslmxdgi.py
# Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_1 => clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul, sub
# Graph fragment:
#   %iota : [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 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 0.4666666666666667), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 0.0), kwargs = {})
#   %sub : [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, 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_arange_clamp_mul_sub_16 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_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=[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_arange_clamp_mul_sub_16', '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_arange_clamp_mul_sub_16(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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/am/camdbf62qsjodxp4vui7qdhsz63wpevku7c42zr5voumc5pfamz7.py
# Topologically Sorted Source Nodes: [up, up_1], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   up => convolution_10
#   up_1 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_3, mul_2, mul_3, mul_4, sub_1, sub_2, sub_4
# Graph fragment:
#   %convolution_10 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_9, %primals_22, %primals_23, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_10, [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 = (%convolution_10, [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 = (%convolution_10, [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 = (%convolution_10, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %sub_1 : [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_1, %clamp_max_2), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
#   %sub_2 : [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_2, %clamp_max_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %add_2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_3), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_17 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_17', 'mutated_arg_names': [], '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_sub_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x5 = (xindex // 256)
    x2 = (xindex // 256) % 512
    x6 = 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')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp34 = 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*x5)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + (8*tmp4) + (64*x5)), None, eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + (8*tmp25) + (64*x5)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + (8*tmp25) + (64*x5)), None, eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x6), tmp21, None)
    tl.store(out_ptr1 + (x6), tmp35, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/j6/cj6fumma62jtj7xlgosx37oeojvmvcdogde4lgj4lvmprc3nh554.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   y => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd, %constant_pad_nd_1], 1), kwargs = {})
triton_poi_fused_cat_18 = async_compile.triton('triton_poi_fused_cat_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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_18(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3211264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 784) % 1024
    x1 = (xindex // 28) % 28
    x0 = xindex % 28
    x3 = (xindex // 802816)
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 24, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = (-2) + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + ((-50) + x0 + (24*x1) + (576*x2) + (294912*x3)), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tmp20 = tl.full([1], 1024, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = (-6) + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 16, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = (-6) + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + ((-102) + x0 + (16*x1) + (256*((-512) + x2)) + (131072*x3)), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + ((-102) + x0 + (16*x1) + (256*((-512) + x2)) + (131072*x3)), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + (x6), tmp40, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ah/cahkyn4sw4pzflccaqi67oh5664xbvsh66vbfhzz4nap6xhmcfos.py
# Topologically Sorted Source Nodes: [up_4], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_4 => 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_19 = async_compile.triton('triton_poi_fused__to_copy_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=[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_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_19(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/xi/cxic34thfzspjsgyjqxbmh6gaz7ugvp6vs6yh4uu7btyupxzd5gq.py
# Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_4 => add_5, clamp_max_4
# Graph fragment:
#   %add_5 : [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_5, 23), kwargs = {})
triton_poi_fused_add_clamp_20 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_20(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 23, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6n/c6nncmrqx3j26y4ejymrtfgrv7csnxfnx56qobcwcgwyo5naliaq.py
# Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_4 => clamp_max_6, clamp_min_4, clamp_min_6, convert_element_type_4, iota_2, mul_5, sub_5
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (48,), 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 = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_4, 0.48936170212765956), kwargs = {})
#   %clamp_min_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_5, 0.0), kwargs = {})
#   %sub_5 : [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_5, 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_arange_clamp_mul_sub_21 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_arange_clamp_mul_sub_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_arange_clamp_mul_sub_21(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/eq/ceqkdehpwy5snfrf4qpyyo7ez6zfnl764nr5dgnig7rrv5yycnab.py
# Topologically Sorted Source Nodes: [up_3, up_4], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   up_3 => convolution_13
#   up_4 => _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, add_7, add_8, mul_7, mul_8, mul_9, sub_6, sub_7, sub_9
# Graph fragment:
#   %convolution_13 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_11, %primals_28, %primals_29, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_13, [None, None, %convert_element_type_5, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_13, [None, None, %convert_element_type_5, %clamp_max_5]), kwargs = {})
#   %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_13, [None, None, %clamp_max_4, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_13, [None, None, %clamp_max_4, %clamp_max_5]), kwargs = {})
#   %sub_6 : [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_6, %clamp_max_6), kwargs = {})
#   %add_7 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_4, %mul_7), kwargs = {})
#   %sub_7 : [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_7, %clamp_max_6), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_6, %mul_8), kwargs = {})
#   %sub_9 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_8, %add_7), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_9, %clamp_max_7), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_22 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_22', 'mutated_arg_names': [], '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_sub_22(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2359296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 48) % 48
    x0 = xindex % 48
    x5 = (xindex // 2304)
    x2 = (xindex // 2304) % 256
    x6 = 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')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 24, 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 + (24*tmp4) + (576*x5)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + (24*tmp4) + (576*x5)), None, eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + (24*tmp25) + (576*x5)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + (24*tmp25) + (576*x5)), None, eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x6), tmp21, None)
    tl.store(out_ptr1 + (x6), tmp35, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/5c/c5cn4aq74h5b35fn34e465tlgyvgb7m5rhrqhufqy67vjucpt2lq.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   y_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd_2, %constant_pad_nd_3], 1), kwargs = {})
triton_poi_fused_cat_23 = async_compile.triton('triton_poi_fused_cat_23', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8388608], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7620608
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 3721) % 512
    x1 = (xindex // 61) % 61
    x0 = xindex % 61
    x3 = (xindex // 1905152)
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 57, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = (-2) + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + ((-116) + x0 + (57*x1) + (3249*x2) + (831744*x3)), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tmp20 = tl.full([1], 512, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = (-6) + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 48, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = (-6) + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + ((-294) + x0 + (48*x1) + (2304*((-256) + x2)) + (589824*x3)), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + ((-294) + x0 + (48*x1) + (2304*((-256) + x2)) + (589824*x3)), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + (x6), tmp40, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ud/cudr7becxwrhayxvgy4twmtw36dur2yzlr2n6im7t3vh6obigpeo.py
# Topologically Sorted Source Nodes: [up_7], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_7 => convert_element_type_9
# Graph fragment:
#   %convert_element_type_9 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_4, torch.int64), kwargs = {})
triton_poi_fused__to_copy_24 = async_compile.triton('triton_poi_fused__to_copy_24', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[128], 
    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_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_24(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ho/chocq4mxa2jyfkljxpgz7j66aolecixsnpic7djgbbj23te3emvz.py
# Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_7 => add_10, clamp_max_8
# Graph fragment:
#   %add_10 : [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_10, 56), kwargs = {})
triton_poi_fused_add_clamp_25 = async_compile.triton('triton_poi_fused_add_clamp_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[128], 
    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_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_25(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 56, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/3g/c3gvi6pzhytun7e2pobs6u22haytd6jdugrt6e3g3zsm2il6ypwa.py
# Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_7 => clamp_max_10, clamp_min_10, clamp_min_8, convert_element_type_8, iota_4, mul_10, sub_10
# Graph fragment:
#   %iota_4 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (114,), 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 = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_8, 0.49557522123893805), kwargs = {})
#   %clamp_min_8 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_10, 0.0), kwargs = {})
#   %sub_10 : [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_10, 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_arange_clamp_mul_sub_26 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_26', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[128], 
    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_arange_clamp_mul_sub_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_26(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/bb/cbbr4a2f45w2kckcum3bwlu3oyqntc7lt52o6que35wlr57fr53f.py
# Topologically Sorted Source Nodes: [up_6, up_7], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   up_6 => convolution_16
#   up_7 => _unsafe_index_10, _unsafe_index_11, _unsafe_index_8, _unsafe_index_9, add_12, add_13, mul_12, mul_13, mul_14, sub_11, sub_12, sub_14
# Graph fragment:
#   %convolution_16 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_13, %primals_34, %primals_35, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index_8 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_16, [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 = (%convolution_16, [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 = (%convolution_16, [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 = (%convolution_16, [None, None, %clamp_max_8, %clamp_max_9]), kwargs = {})
#   %sub_11 : [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_11, %clamp_max_10), kwargs = {})
#   %add_12 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_8, %mul_12), kwargs = {})
#   %sub_12 : [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_12, %clamp_max_10), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_10, %mul_13), kwargs = {})
#   %sub_14 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_13, %add_12), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_14, %clamp_max_11), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_27 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_27', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8388608], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_27', 'mutated_arg_names': [], '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_sub_27(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6653952
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 114) % 114
    x0 = xindex % 114
    x5 = (xindex // 12996)
    x2 = (xindex // 12996) % 128
    x4 = xindex % 12996
    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')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 57, 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 + (57*tmp4) + (3249*x5)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + (57*tmp4) + (3249*x5)), None, eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + (57*tmp25) + (3249*x5)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + (57*tmp25) + (3249*x5)), None, eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x4 + (13024*x5)), tmp21, None)
    tl.store(out_ptr1 + (x4 + (13024*x5)), tmp35, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cf/ccf6zb432yqegq3mo33rlgwbrjbposwbqnzspijr3uqwwnrohtuk.py
# Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   y_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd_4, %constant_pad_nd_5], 1), kwargs = {})
triton_poi_fused_cat_28 = async_compile.triton('triton_poi_fused_cat_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=[16777216], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_28', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_28(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16257024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 15876) % 256
    x1 = (xindex // 126) % 126
    x0 = xindex % 126
    x3 = (xindex // 4064256)
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 122, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = (-2) + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + ((-246) + x0 + (122*x1) + (14884*x2) + (1905152*x3)), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tmp20 = tl.full([1], 256, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = (-6) + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 114, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = (-6) + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + ((-690) + x0 + (114*x1) + (13024*((-128) + x2)) + (1667072*x3)), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + ((-690) + x0 + (114*x1) + (13024*((-128) + x2)) + (1667072*x3)), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + (x6), tmp40, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6b/c6bmihke4kvjmmonimin5iqf2l5owtisnfmxujwn66euncqmopwp.py
# Topologically Sorted Source Nodes: [up_10], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_10 => 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_29 = async_compile.triton('triton_poi_fused__to_copy_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=[256], 
    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_29', '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_29(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/f3/cf3mmi6ihum72e7m3cgzwt4plrw6fkozmpvnsinv42hinztv4g4e.py
# Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_10 => add_15, clamp_max_12
# Graph fragment:
#   %add_15 : [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_15, 121), kwargs = {})
triton_poi_fused_add_clamp_30 = async_compile.triton('triton_poi_fused_add_clamp_30', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    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_30', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_30(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 121, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6q/c6qs74ukxhcr335sstfbzfcipfclkqsrfjds7rsaqceos2ks56sc.py
# Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_10 => clamp_max_14, clamp_min_12, clamp_min_14, convert_element_type_12, iota_6, mul_15, sub_15
# Graph fragment:
#   %iota_6 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (244,), 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 = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_12, 0.49794238683127573), kwargs = {})
#   %clamp_min_12 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_15, 0.0), kwargs = {})
#   %sub_15 : [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_15, 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_arange_clamp_mul_sub_31 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_31', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    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_arange_clamp_mul_sub_31', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_31(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/26/c26l3cuzpdj6e65ngbevraq3jvcmshya5rauf2acqpfu7stjo463.py
# Topologically Sorted Source Nodes: [up_9, up_10], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   up_10 => _unsafe_index_12, _unsafe_index_13, _unsafe_index_14, _unsafe_index_15, add_17, add_18, mul_17, mul_18, mul_19, sub_16, sub_17, sub_19
#   up_9 => convolution_19
# Graph fragment:
#   %convolution_19 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_15, %primals_40, %primals_41, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %_unsafe_index_12 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution_19, [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 = (%convolution_19, [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 = (%convolution_19, [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 = (%convolution_19, [None, None, %clamp_max_12, %clamp_max_13]), kwargs = {})
#   %sub_16 : [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_16, %clamp_max_14), kwargs = {})
#   %add_17 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_12, %mul_17), kwargs = {})
#   %sub_17 : [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_17, %clamp_max_14), kwargs = {})
#   %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_14, %mul_18), kwargs = {})
#   %sub_19 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_18, %add_17), kwargs = {})
#   %mul_19 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_19, %clamp_max_15), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_sub_32 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_sub_32', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16777216], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_sub_32', 'mutated_arg_names': [], '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_sub_32(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 15241216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 244) % 244
    x0 = xindex % 244
    x5 = (xindex // 59536)
    x2 = (xindex // 59536) % 64
    x4 = xindex % 59536
    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')
    tmp12 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 122, 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 + (122*tmp4) + (14884*x5)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + (122*tmp4) + (14884*x5)), None, eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + (122*tmp25) + (14884*x5)), None, eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + (122*tmp25) + (14884*x5)), None, eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x4 + (59552*x5)), tmp21, None)
    tl.store(out_ptr1 + (x4 + (59552*x5)), tmp35, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/65/c65rs5r3w3a6ubiwogqsdxqagoviy5beimxot6eqavdlbw6ctaq7.py
# Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   y_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd_6, %constant_pad_nd_7], 1), kwargs = {})
triton_poi_fused_cat_33 = async_compile.triton('triton_poi_fused_cat_33', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[33554432], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_33', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_33(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 33554432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 65536) % 128
    x1 = (xindex // 256) % 256
    x0 = xindex % 256
    x3 = (xindex // 8388608)
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 252, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = (-2) + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + ((-506) + x0 + (252*x1) + (63504*x2) + (4064256*x3)), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tmp20 = tl.full([1], 128, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = (-6) + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 244, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = (-6) + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + ((-1470) + x0 + (244*x1) + (59552*((-64) + x2)) + (3811328*x3)), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + ((-1470) + x0 + (244*x1) + (59552*((-64) + x2)) + (3811328*x3)), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + (x6), tmp40, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cx/ccxkslerd5uympqutx2sq3qawltualucrkshks5e4i3jvdocgsfb.py
# Topologically Sorted Source Nodes: [x_34, x_35, x_36], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_34 => convolution_21
#   x_35 => relu_17
#   x_36 => constant_pad_nd_8
# Graph fragment:
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_16, %primals_44, %primals_45, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_17 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_21,), kwargs = {})
#   %constant_pad_nd_8 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu_17, [2, 2, 2, 2], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_relu_34 = async_compile.triton('triton_poi_fused_constant_pad_nd_convolution_relu_34', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16777216], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_constant_pad_nd_convolution_relu_34', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_relu_34(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16777216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 256) % 256
    x0 = xindex % 256
    x4 = (xindex // 65536)
    x2 = (xindex // 65536) % 64
    x6 = xindex
    tmp0 = (-2) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 252, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-506) + x0 + (252*x1) + (63504*x4)), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x2), tmp10, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + (x6), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vi/cvi4dkwbyi6tdqqb6cbkh72tumog2lpckmntmcyjprawkjtaan3l.py
# Topologically Sorted Source Nodes: [x_37], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_37 => convolution_22
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_8, %primals_46, %primals_47, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_35 = async_compile.triton('triton_poi_fused_convolution_35', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_35', '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_35(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 // 65536) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/r2/cr2guae7qgv2b5k5c63deh3xw6trpnrebttcw3ysc43mp3xobt77.py
# Topologically Sorted Source Nodes: [x_34, x_35], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_34 => convolution_21
#   x_35 => relu_17
# Graph fragment:
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_16, %primals_44, %primals_45, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_17 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_21,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_17, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_36 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_36', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16777216], 
    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_36', '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_36(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16257024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 63504) % 64
    x0 = xindex % 63504
    x4 = (xindex // 63504)
    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 + (x0 + (63616*x4)), 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 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 256, 256), (262144, 65536, 256, 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, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (512, ), (1, ))
    assert_size_stride(primals_16, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_17, (512, ), (1, ))
    assert_size_stride(primals_18, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_19, (1024, ), (1, ))
    assert_size_stride(primals_20, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_21, (1024, ), (1, ))
    assert_size_stride(primals_22, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_23, (512, ), (1, ))
    assert_size_stride(primals_24, (512, 1024, 3, 3), (9216, 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, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (256, ), (1, ))
    assert_size_stride(primals_30, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (256, ), (1, ))
    assert_size_stride(primals_32, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_33, (256, ), (1, ))
    assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (128, ), (1, ))
    assert_size_stride(primals_36, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (128, ), (1, ))
    assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (128, ), (1, ))
    assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_41, (64, ), (1, ))
    assert_size_stride(primals_42, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_43, (64, ), (1, ))
    assert_size_stride(primals_44, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (64, ), (1, ))
    assert_size_stride(primals_46, (4, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_47, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 254, 254), (4129024, 64516, 254, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 16516096, grid=grid(16516096), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 252, 252), (4064256, 63504, 252, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 16257024, grid=grid(16257024), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 126, 126), (1017856, 15904, 126, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 126, 126), (1024000, 16000, 126, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_2.run(buf3, buf4, buf5, 4064256, grid=grid(4064256), stream=stream0)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 124, 124), (1968128, 15376, 124, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf7, primals_7, 7872512, grid=grid(7872512), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 128, 122, 122), (1905152, 14884, 122, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf9, primals_9, 7620608, grid=grid(7620608), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 61, 61), (479232, 3744, 61, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 128, 61, 61), (491520, 3840, 61, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_5.run(buf9, buf10, buf11, 1905152, grid=grid(1905152), stream=stream0)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 59, 59), (891136, 3481, 59, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf13, primals_11, 3564544, grid=grid(3564544), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 256, 57, 57), (831744, 3249, 57, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_7.run(buf15, primals_13, 3326976, grid=grid(3326976), stream=stream0)
        del primals_13
        buf16 = empty_strided_cuda((4, 256, 28, 28), (200704, 784, 28, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 256, 28, 28), (200704, 784, 28, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_8.run(buf15, buf16, buf17, 802816, grid=grid(802816), stream=stream0)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 512, 26, 26), (346112, 676, 26, 1))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [x_12, x_13], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf19, primals_15, 1384448, grid=grid(1384448), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 512, 24, 24), (294912, 576, 24, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [x_14, x_15], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf21, primals_17, 1179648, grid=grid(1179648), stream=stream0)
        del primals_17
        buf22 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_11.run(buf21, buf22, buf23, 294912, grid=grid(294912), stream=stream0)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf22, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 1024, 10, 10), (102400, 100, 10, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [x_16, x_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf25, primals_19, 409600, grid=grid(409600), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 1024, 8, 8), (65536, 64, 8, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [x_18, x_19], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf27, primals_21, 262144, grid=grid(262144), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, 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, 8, 8), (32768, 64, 8, 1))
        buf29 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_14.run(buf29, 16, grid=grid(16), stream=stream0)
        buf30 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_15.run(buf30, 16, grid=grid(16), stream=stream0)
        buf31 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_14.run(buf31, 16, grid=grid(16), stream=stream0)
        buf32 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_15.run(buf32, 16, grid=grid(16), stream=stream0)
        buf33 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_16.run(buf33, 16, grid=grid(16), stream=stream0)
        buf35 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_16.run(buf35, 16, grid=grid(16), stream=stream0)
        buf34 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1), torch.float32)
        buf36 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up, up_1], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_17.run(buf29, buf31, buf28, primals_23, buf32, buf33, buf30, buf35, buf34, buf36, 524288, grid=grid(524288), stream=stream0)
        del buf28
        del primals_23
        buf37 = empty_strided_cuda((4, 1024, 28, 28), (802816, 784, 28, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.cat]
        triton_poi_fused_cat_18.run(buf21, buf34, buf36, buf37, 3211264, grid=grid(3211264), stream=stream0)
        del buf34
        del buf36
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 26, 26), (346112, 676, 26, 1))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [x_20, x_21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf39, primals_25, 1384448, grid=grid(1384448), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 512, 24, 24), (294912, 576, 24, 1))
        buf41 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [x_22, x_23], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf41, primals_27, 1179648, grid=grid(1179648), stream=stream0)
        del primals_27
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 24, 24), (147456, 576, 24, 1))
        buf43 = empty_strided_cuda((48, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_19.run(buf43, 48, grid=grid(48), stream=stream0)
        buf44 = empty_strided_cuda((48, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_20.run(buf44, 48, grid=grid(48), stream=stream0)
        buf45 = empty_strided_cuda((48, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_19.run(buf45, 48, grid=grid(48), stream=stream0)
        buf46 = empty_strided_cuda((48, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_20.run(buf46, 48, grid=grid(48), stream=stream0)
        buf47 = empty_strided_cuda((48, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21.run(buf47, 48, grid=grid(48), stream=stream0)
        buf49 = empty_strided_cuda((48, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_4], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21.run(buf49, 48, grid=grid(48), stream=stream0)
        buf48 = empty_strided_cuda((4, 256, 48, 48), (589824, 2304, 48, 1), torch.float32)
        buf50 = empty_strided_cuda((4, 256, 48, 48), (589824, 2304, 48, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_3, up_4], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_22.run(buf43, buf45, buf42, primals_29, buf46, buf47, buf44, buf49, buf48, buf50, 2359296, grid=grid(2359296), stream=stream0)
        del buf42
        del primals_29
        buf51 = empty_strided_cuda((4, 512, 61, 61), (1905152, 3721, 61, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_23.run(buf15, buf48, buf50, buf51, 7620608, grid=grid(7620608), stream=stream0)
        del buf48
        del buf50
        # Topologically Sorted Source Nodes: [x_24], Original ATen: [aten.convolution]
        buf52 = extern_kernels.convolution(buf51, primals_30, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 256, 59, 59), (891136, 3481, 59, 1))
        buf53 = buf52; del buf52  # reuse
        # Topologically Sorted Source Nodes: [x_24, x_25], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf53, primals_31, 3564544, grid=grid(3564544), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [x_26], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf53, primals_32, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 256, 57, 57), (831744, 3249, 57, 1))
        buf55 = buf54; del buf54  # reuse
        # Topologically Sorted Source Nodes: [x_26, x_27], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_7.run(buf55, primals_33, 3326976, grid=grid(3326976), stream=stream0)
        del primals_33
        # Topologically Sorted Source Nodes: [up_6], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 128, 57, 57), (415872, 3249, 57, 1))
        buf57 = empty_strided_cuda((114, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_24.run(buf57, 114, grid=grid(114), stream=stream0)
        buf58 = empty_strided_cuda((114, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_25.run(buf58, 114, grid=grid(114), stream=stream0)
        buf59 = empty_strided_cuda((114, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_24.run(buf59, 114, grid=grid(114), stream=stream0)
        buf60 = empty_strided_cuda((114, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_25.run(buf60, 114, grid=grid(114), stream=stream0)
        buf61 = empty_strided_cuda((114, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_26.run(buf61, 114, grid=grid(114), stream=stream0)
        buf63 = empty_strided_cuda((114, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_7], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_26.run(buf63, 114, grid=grid(114), stream=stream0)
        buf62 = empty_strided_cuda((4, 128, 114, 114), (1667072, 13024, 114, 1), torch.float32)
        buf64 = empty_strided_cuda((4, 128, 114, 114), (1667072, 13024, 114, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_6, up_7], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_27.run(buf57, buf59, buf56, primals_35, buf60, buf61, buf58, buf63, buf62, buf64, 6653952, grid=grid(6653952), stream=stream0)
        del buf56
        del primals_35
        buf65 = empty_strided_cuda((4, 256, 126, 126), (4064256, 15876, 126, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_28.run(buf9, buf62, buf64, buf65, 16257024, grid=grid(16257024), stream=stream0)
        del buf62
        del buf64
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, primals_36, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 124, 124), (1968128, 15376, 124, 1))
        buf67 = buf66; del buf66  # reuse
        # Topologically Sorted Source Nodes: [x_28, x_29], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf67, primals_37, 7872512, grid=grid(7872512), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.convolution]
        buf68 = extern_kernels.convolution(buf67, primals_38, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 128, 122, 122), (1905152, 14884, 122, 1))
        buf69 = buf68; del buf68  # reuse
        # Topologically Sorted Source Nodes: [x_30, x_31], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf69, primals_39, 7620608, grid=grid(7620608), stream=stream0)
        del primals_39
        # Topologically Sorted Source Nodes: [up_9], Original ATen: [aten.convolution]
        buf70 = extern_kernels.convolution(buf69, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 64, 122, 122), (952576, 14884, 122, 1))
        buf71 = empty_strided_cuda((244, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_29.run(buf71, 244, grid=grid(244), stream=stream0)
        buf72 = empty_strided_cuda((244, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_30.run(buf72, 244, grid=grid(244), stream=stream0)
        buf73 = empty_strided_cuda((244, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_29.run(buf73, 244, grid=grid(244), stream=stream0)
        buf74 = empty_strided_cuda((244, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_30.run(buf74, 244, grid=grid(244), stream=stream0)
        buf75 = empty_strided_cuda((244, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_31.run(buf75, 244, grid=grid(244), stream=stream0)
        buf77 = empty_strided_cuda((244, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_10], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_31.run(buf77, 244, grid=grid(244), stream=stream0)
        buf76 = empty_strided_cuda((4, 64, 244, 244), (3811328, 59552, 244, 1), torch.float32)
        buf78 = empty_strided_cuda((4, 64, 244, 244), (3811328, 59552, 244, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_9, up_10], Original ATen: [aten.convolution, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_32.run(buf71, buf73, buf70, primals_41, buf74, buf75, buf72, buf77, buf76, buf78, 15241216, grid=grid(15241216), stream=stream0)
        del buf70
        del primals_41
        buf79 = empty_strided_cuda((4, 128, 256, 256), (8388608, 65536, 256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_33.run(buf3, buf76, buf78, buf79, 33554432, grid=grid(33554432), stream=stream0)
        del buf76
        del buf78
        # Topologically Sorted Source Nodes: [x_32], Original ATen: [aten.convolution]
        buf80 = extern_kernels.convolution(buf79, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf80, (4, 64, 254, 254), (4129024, 64516, 254, 1))
        buf81 = buf80; del buf80  # reuse
        # Topologically Sorted Source Nodes: [x_32, x_33], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf81, primals_43, 16516096, grid=grid(16516096), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [x_34], Original ATen: [aten.convolution]
        buf82 = extern_kernels.convolution(buf81, primals_44, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf82, (4, 64, 252, 252), (4064256, 63504, 252, 1))
        buf83 = empty_strided_cuda((4, 64, 256, 256), (4194304, 65536, 256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_34, x_35, x_36], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_relu_34.run(buf82, primals_45, buf83, 16777216, grid=grid(16777216), stream=stream0)
        # Topologically Sorted Source Nodes: [x_37], Original ATen: [aten.convolution]
        buf84 = extern_kernels.convolution(buf83, primals_46, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf84, (4, 4, 256, 256), (262144, 65536, 256, 1))
        buf85 = buf84; del buf84  # reuse
        # Topologically Sorted Source Nodes: [x_37], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_35.run(buf85, primals_47, 1048576, grid=grid(1048576), stream=stream0)
        del primals_47
        buf86 = empty_strided_cuda((4, 64, 252, 252), (4071424, 63616, 252, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_34, x_35], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_36.run(buf82, primals_45, buf86, 16257024, grid=grid(16257024), stream=stream0)
        del buf82
        del primals_45
    return (buf85, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, primals_32, primals_34, primals_36, primals_38, primals_40, primals_42, primals_44, primals_46, buf1, buf3, buf4, buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf29, buf30, buf31, buf32, buf33, buf35, buf37, buf39, buf41, buf43, buf44, buf45, buf46, buf47, buf49, buf51, buf53, buf55, buf57, buf58, buf59, buf60, buf61, buf63, buf65, buf67, buf69, buf71, buf72, buf73, buf74, buf75, buf77, buf79, buf81, buf83, buf86, )


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, 4, 3, 3), (36, 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, 4, 256, 256), (262144, 65536, 256, 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((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((512, 512, 3, 3), (4608, 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((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((1024, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((512, 1024, 3, 3), (9216, 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, 1024, 3, 3), (9216, 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((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((4, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class DoubleConv(nn.Module):
    """
    Double 3x3 conv + relu
    """

    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv_1 = nn.Conv2d(in_channels, out_channels, 3)
        self.conv_2 = nn.Conv2d(out_channels, out_channels, 3)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv_1(x)
        x = self.relu(x)
        x = self.conv_2(x)
        x = self.relu(x)
        return x


class UpsampleCat(nn.Module):
    """
    Unsample input and concat with contracting tensor
    """

    def __init__(self, ch):
        super(UpsampleCat, self).__init__()
        self.up_conv = nn.Conv2d(ch, ch // 2, 3, padding=1)
        self.up = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)

    def forward(self, up, down):
        up = self.up_conv(up)
        up = self.up(up)
        up_w, up_h = up.size()[2:4]
        down_w, down_h = down.size()[2:4]
        dw = down_w + 4 - up_w
        dh = down_h + 4 - up_h
        down = F.pad(down, (2, 2, 2, 2))
        up = F.pad(up, (dw // 2, dw - dw // 2, dh // 2, dh - dh // 2))
        y = torch.cat([down, up], dim=1)
        return y


class UNet(nn.Module):
    """
    UNet model
    """

    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()
        self.conv_1 = DoubleConv(in_channels, 64)
        self.conv_2 = DoubleConv(64, 128)
        self.conv_3 = DoubleConv(128, 256)
        self.conv_4 = DoubleConv(256, 512)
        self.conv_5 = DoubleConv(512, 1024)
        self.down = nn.MaxPool2d(2)
        self.up_1 = UpsampleCat(1024)
        self.up_2 = UpsampleCat(512)
        self.up_3 = UpsampleCat(256)
        self.up_4 = UpsampleCat(128)
        self.conv_6 = DoubleConv(1024, 512)
        self.conv_7 = DoubleConv(512, 256)
        self.conv_8 = DoubleConv(256, 128)
        self.conv_9 = DoubleConv(128, 64)
        self.out_conv = nn.Conv2d(64, out_channels, 1)

    def forward(self, x):
        x1 = self.conv_1(x)
        x2 = self.conv_2(self.down(x1))
        x3 = self.conv_3(self.down(x2))
        x4 = self.conv_4(self.down(x3))
        x = self.conv_5(self.down(x4))
        x = self.conv_6(self.up_1(x, x4))
        x = self.conv_7(self.up_2(x, x3))
        x = self.conv_8(self.up_3(x, x2))
        x = self.conv_9(self.up_4(x, x1))
        x = F.pad(x, (2, 2, 2, 2))
        x = self.out_conv(x)
        return x


def get_inputs():
    return [torch.rand([4, 4, 256, 256])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

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


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16516096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64516 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(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 // 63504 % 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_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4064256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 126
    x3 = xindex // 126
    x2 = xindex // 15876
    x4 = xindex % 15876
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 504 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 504 * x3), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (252 + 2 * x0 + 504 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (253 + 2 * x0 + 504 * x3), xmask,
        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 + (x4 + 15904 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + 16000 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 15376 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_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 // 14884 % 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_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1905152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x3 = xindex // 61
    x2 = xindex // 3721
    x4 = xindex % 3721
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 244 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 244 * x3), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (122 + 2 * x0 + 244 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (123 + 2 * x0 + 244 * x3), xmask,
        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 + (x4 + 3744 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + 3840 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3564544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3481 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3326976
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3249 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_8(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 % 28
    x1 = xindex // 28 % 28
    x2 = xindex // 784
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 114 * x1 + 3249 * x2), None,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 114 * x1 + 3249 * x2), None,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (57 + 2 * x0 + 114 * x1 + 3249 * x2), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (58 + 2 * x0 + 114 * x1 + 3249 * x2), 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 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 676 % 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_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 576 % 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_11(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 % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 48 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 48 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (24 + 2 * x0 + 48 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (25 + 2 * x0 + 48 * 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_12(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 100 % 1024
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 1024
    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_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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 7, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_16(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 = 0.4666666666666667
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_17(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x5 = xindex // 256
    x2 = xindex // 256 % 512
    x6 = 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')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp34 = 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 * x5), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 8 * tmp4 + 64 * x5), None,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 8 * tmp25 + 64 * x5), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 8 * tmp25 + 64 * x5), None,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + x6, tmp21, None)
    tl.store(out_ptr1 + x6, tmp35, None)


@triton.jit
def triton_poi_fused_cat_18(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 784 % 1024
    x1 = xindex // 28 % 28
    x0 = xindex % 28
    x3 = xindex // 802816
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 24, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = -2 + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + (-50 + x0 + 24 * x1 + 576 * x2 + 294912 * x3),
        tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 1024, tl.int64)
    tmp22 = -6 + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 16, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = -6 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + (-102 + x0 + 16 * x1 + 256 * (-512 + x2) + 
        131072 * x3), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + (-102 + x0 + 16 * x1 + 256 * (-512 + x2) + 
        131072 * x3), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + x6, tmp40, None)


@triton.jit
def triton_poi_fused__to_copy_19(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_20(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 23, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_21(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    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 = 0.48936170212765956
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_22(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = xindex // 48 % 48
    x0 = xindex % 48
    x5 = xindex // 2304
    x2 = xindex // 2304 % 256
    x6 = 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')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 24, 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 + 24 * tmp4 + 576 * x5), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 24 * tmp4 + 576 * x5), None,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 24 * tmp25 + 576 * x5), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 24 * tmp25 + 576 * x5), None,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + x6, tmp21, None)
    tl.store(out_ptr1 + x6, tmp35, None)


@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 3721 % 512
    x1 = xindex // 61 % 61
    x0 = xindex % 61
    x3 = xindex // 1905152
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 57, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = -2 + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + (-116 + x0 + 57 * x1 + 3249 * x2 + 831744 *
        x3), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp22 = -6 + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 48, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = -6 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + (-294 + x0 + 48 * x1 + 2304 * (-256 + x2) + 
        589824 * x3), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + (-294 + x0 + 48 * x1 + 2304 * (-256 + x2) + 
        589824 * x3), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + x6, tmp40, None)


@triton.jit
def triton_poi_fused__to_copy_24(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_25(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 56, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_26(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 114
    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 = 0.49557522123893805
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_27(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = xindex // 114 % 114
    x0 = xindex % 114
    x5 = xindex // 12996
    x2 = xindex // 12996 % 128
    x4 = xindex % 12996
    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')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 57, 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 + 57 * tmp4 + 3249 * x5), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 57 * tmp4 + 3249 * x5), None,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 57 * tmp25 + 3249 * x5), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 57 * tmp25 + 3249 * x5), None,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x4 + 13024 * x5), tmp21, None)
    tl.store(out_ptr1 + (x4 + 13024 * x5), tmp35, None)


@triton.jit
def triton_poi_fused_cat_28(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 15876 % 256
    x1 = xindex // 126 % 126
    x0 = xindex % 126
    x3 = xindex // 4064256
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 122, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = -2 + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + (-246 + x0 + 122 * x1 + 14884 * x2 + 1905152 *
        x3), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp22 = -6 + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 114, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = -6 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + (-690 + x0 + 114 * x1 + 13024 * (-128 + x2) +
        1667072 * x3), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + (-690 + x0 + 114 * x1 + 13024 * (-128 + x2) +
        1667072 * x3), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + x6, tmp40, None)


@triton.jit
def triton_poi_fused__to_copy_29(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_30(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 121, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_31(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 244
    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 = 0.49794238683127573
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = 1.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_32(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = xindex // 244 % 244
    x0 = xindex % 244
    x5 = xindex // 59536
    x2 = xindex // 59536 % 64
    x4 = xindex % 59536
    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')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 122, 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 + 122 * tmp4 + 14884 * x5), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 122 * tmp4 + 14884 * x5), None,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 122 * tmp25 + 14884 * x5), None,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 122 * tmp25 + 14884 * x5), None,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tl.store(out_ptr0 + (x4 + 59552 * x5), tmp21, None)
    tl.store(out_ptr1 + (x4 + 59552 * x5), tmp35, None)


@triton.jit
def triton_poi_fused_cat_33(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 65536 % 128
    x1 = xindex // 256 % 256
    x0 = xindex % 256
    x3 = xindex // 8388608
    x6 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 252, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = -2 + x0
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp7
    tmp12 = tmp6 & tmp8
    tmp13 = tmp12 & tmp10
    tmp14 = tmp13 & tmp11
    tmp15 = tmp14 & tmp4
    tmp16 = tl.load(in_ptr0 + (-506 + x0 + 252 * x1 + 63504 * x2 + 4064256 *
        x3), tmp15, other=0.0)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp22 = -6 + x1
    tmp23 = tmp22 >= tmp1
    tmp24 = tl.full([1], 244, tl.int64)
    tmp25 = tmp22 < tmp24
    tmp26 = -6 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp24
    tmp29 = tmp23 & tmp25
    tmp30 = tmp29 & tmp27
    tmp31 = tmp30 & tmp28
    tmp32 = tmp31 & tmp19
    tmp33 = tl.load(in_ptr1 + (-1470 + x0 + 244 * x1 + 59552 * (-64 + x2) +
        3811328 * x3), tmp32, other=0.0)
    tmp34 = tl.load(in_ptr2 + (-1470 + x0 + 244 * x1 + 59552 * (-64 + x2) +
        3811328 * x3), tmp32, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp32, tmp35, tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp19, tmp37, tmp38)
    tmp40 = tl.where(tmp4, tmp18, tmp39)
    tl.store(out_ptr0 + x6, tmp40, None)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_relu_34(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 256 % 256
    x0 = xindex % 256
    x4 = xindex // 65536
    x2 = xindex // 65536 % 64
    x6 = xindex
    tmp0 = -2 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 252, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-506 + x0 + 252 * x1 + 63504 * x4), tmp10,
        other=0.0)
    tmp12 = tl.load(in_ptr1 + x2, tmp10, eviction_policy='evict_last',
        other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + x6, tmp17, None)


@triton.jit
def triton_poi_fused_convolution_35(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 // 65536 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_36(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 // 63504 % 64
    x0 = xindex % 63504
    x4 = xindex // 63504
    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 + (x0 + 63616 * x4), 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) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 256, 256), (262144, 65536, 256, 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, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (512,), (1,))
    assert_size_stride(primals_16, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_19, (1024,), (1,))
    assert_size_stride(primals_20, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_21, (1024,), (1,))
    assert_size_stride(primals_22, (512, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_23, (512,), (1,))
    assert_size_stride(primals_24, (512, 1024, 3, 3), (9216, 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, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (256,), (1,))
    assert_size_stride(primals_30, (256, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_31, (256,), (1,))
    assert_size_stride(primals_32, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_33, (256,), (1,))
    assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_35, (128,), (1,))
    assert_size_stride(primals_36, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (128,), (1,))
    assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (128,), (1,))
    assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_41, (64,), (1,))
    assert_size_stride(primals_42, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_43, (64,), (1,))
    assert_size_stride(primals_44, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (64,), (1,))
    assert_size_stride(primals_46, (4, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_47, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 254, 254), (4129024, 64516, 254, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(16516096)](buf1, primals_2,
            16516096, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 252, 252), (4064256, 63504, 252, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(16257024)](buf3, primals_5,
            16257024, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 126, 126), (1017856, 15904, 126, 
            1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 126, 126), (1024000, 16000, 126, 
            1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(4064256)](buf3,
            buf4, buf5, 4064256, XBLOCK=512, num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 124, 124), (1968128, 15376, 124, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(7872512)](buf7, primals_7,
            7872512, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 128, 122, 122), (1905152, 14884, 122, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_4[grid(7620608)](buf9, primals_9,
            7620608, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 61, 61), (479232, 3744, 61, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 128, 61, 61), (491520, 3840, 61, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_5[grid(1905152)](buf9,
            buf10, buf11, 1905152, XBLOCK=512, num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 59, 59), (891136, 3481, 59, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_6[grid(3564544)](buf13,
            primals_11, 3564544, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 256, 57, 57), (831744, 3249, 57, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_7[grid(3326976)](buf15,
            primals_13, 3326976, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_13
        buf16 = empty_strided_cuda((4, 256, 28, 28), (200704, 784, 28, 1),
            torch.float32)
        buf17 = empty_strided_cuda((4, 256, 28, 28), (200704, 784, 28, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_8[grid(802816)](buf15,
            buf16, buf17, 802816, XBLOCK=512, num_warps=8, num_stages=1)
        buf18 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 512, 26, 26), (346112, 676, 26, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_9[grid(1384448)](buf19,
            primals_15, 1384448, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_15
        buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 512, 24, 24), (294912, 576, 24, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_10[grid(1179648)](buf21,
            primals_17, 1179648, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf22 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1),
            torch.float32)
        buf23 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_11[grid(294912)](buf21,
            buf22, buf23, 294912, XBLOCK=512, num_warps=8, num_stages=1)
        buf24 = extern_kernels.convolution(buf22, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 1024, 10, 10), (102400, 100, 10, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_12[grid(409600)](buf25,
            primals_19, 409600, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_19
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 1024, 8, 8), (65536, 64, 8, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_13[grid(262144)](buf27,
            primals_21, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_21
        buf28 = extern_kernels.convolution(buf27, 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, 8, 8), (32768, 64, 8, 1))
        buf29 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_14[grid(16)](buf29, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf30 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_15[grid(16)](buf30, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf31 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_14[grid(16)](buf31, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf32 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_add_clamp_15[grid(16)](buf32, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf33 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_16[grid(16)](buf33, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf35 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_16[grid(16)](buf35, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf34 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1),
            torch.float32)
        buf36 = empty_strided_cuda((4, 512, 16, 16), (131072, 256, 16, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_17[grid(524288)
            ](buf29, buf31, buf28, primals_23, buf32, buf33, buf30, buf35,
            buf34, buf36, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf28
        del primals_23
        buf37 = empty_strided_cuda((4, 1024, 28, 28), (802816, 784, 28, 1),
            torch.float32)
        triton_poi_fused_cat_18[grid(3211264)](buf21, buf34, buf36, buf37, 
            3211264, XBLOCK=512, num_warps=8, num_stages=1)
        del buf34
        del buf36
        buf38 = extern_kernels.convolution(buf37, primals_24, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 26, 26), (346112, 676, 26, 1))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_relu_9[grid(1384448)](buf39,
            primals_25, 1384448, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_25
        buf40 = extern_kernels.convolution(buf39, primals_26, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 512, 24, 24), (294912, 576, 24, 1))
        buf41 = buf40
        del buf40
        triton_poi_fused_convolution_relu_10[grid(1179648)](buf41,
            primals_27, 1179648, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_27
        buf42 = extern_kernels.convolution(buf41, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 24, 24), (147456, 576, 24, 1))
        buf43 = empty_strided_cuda((48, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_19[grid(48)](buf43, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf44 = empty_strided_cuda((48, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_20[grid(48)](buf44, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf45 = empty_strided_cuda((48,), (1,), torch.int64)
        triton_poi_fused__to_copy_19[grid(48)](buf45, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf46 = empty_strided_cuda((48,), (1,), torch.int64)
        triton_poi_fused_add_clamp_20[grid(48)](buf46, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf47 = empty_strided_cuda((48,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(48)](buf47, 
            48, XBLOCK=64, num_warps=1, num_stages=1)
        buf49 = empty_strided_cuda((48, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(48)](buf49, 
            48, XBLOCK=64, num_warps=1, num_stages=1)
        buf48 = empty_strided_cuda((4, 256, 48, 48), (589824, 2304, 48, 1),
            torch.float32)
        buf50 = empty_strided_cuda((4, 256, 48, 48), (589824, 2304, 48, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_22[grid(2359296)
            ](buf43, buf45, buf42, primals_29, buf46, buf47, buf44, buf49,
            buf48, buf50, 2359296, XBLOCK=512, num_warps=8, num_stages=1)
        del buf42
        del primals_29
        buf51 = empty_strided_cuda((4, 512, 61, 61), (1905152, 3721, 61, 1),
            torch.float32)
        triton_poi_fused_cat_23[grid(7620608)](buf15, buf48, buf50, buf51, 
            7620608, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf48
        del buf50
        buf52 = extern_kernels.convolution(buf51, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf52, (4, 256, 59, 59), (891136, 3481, 59, 1))
        buf53 = buf52
        del buf52
        triton_poi_fused_convolution_relu_6[grid(3564544)](buf53,
            primals_31, 3564544, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_31
        buf54 = extern_kernels.convolution(buf53, primals_32, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 256, 57, 57), (831744, 3249, 57, 1))
        buf55 = buf54
        del buf54
        triton_poi_fused_convolution_relu_7[grid(3326976)](buf55,
            primals_33, 3326976, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_33
        buf56 = extern_kernels.convolution(buf55, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 128, 57, 57), (415872, 3249, 57, 1))
        buf57 = empty_strided_cuda((114, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_24[grid(114)](buf57, 114, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf58 = empty_strided_cuda((114, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_25[grid(114)](buf58, 114, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf59 = empty_strided_cuda((114,), (1,), torch.int64)
        triton_poi_fused__to_copy_24[grid(114)](buf59, 114, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf60 = empty_strided_cuda((114,), (1,), torch.int64)
        triton_poi_fused_add_clamp_25[grid(114)](buf60, 114, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf61 = empty_strided_cuda((114,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_26[grid(114)](buf61,
            114, XBLOCK=128, num_warps=4, num_stages=1)
        buf63 = empty_strided_cuda((114, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_26[grid(114)](buf63,
            114, XBLOCK=128, num_warps=4, num_stages=1)
        buf62 = empty_strided_cuda((4, 128, 114, 114), (1667072, 13024, 114,
            1), torch.float32)
        buf64 = empty_strided_cuda((4, 128, 114, 114), (1667072, 13024, 114,
            1), torch.float32)
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_27[grid(6653952)
            ](buf57, buf59, buf56, primals_35, buf60, buf61, buf58, buf63,
            buf62, buf64, 6653952, XBLOCK=512, num_warps=8, num_stages=1)
        del buf56
        del primals_35
        buf65 = empty_strided_cuda((4, 256, 126, 126), (4064256, 15876, 126,
            1), torch.float32)
        triton_poi_fused_cat_28[grid(16257024)](buf9, buf62, buf64, buf65, 
            16257024, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf62
        del buf64
        buf66 = extern_kernels.convolution(buf65, primals_36, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 124, 124), (1968128, 15376, 124, 1))
        buf67 = buf66
        del buf66
        triton_poi_fused_convolution_relu_3[grid(7872512)](buf67,
            primals_37, 7872512, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_37
        buf68 = extern_kernels.convolution(buf67, primals_38, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 128, 122, 122), (1905152, 14884, 122, 1))
        buf69 = buf68
        del buf68
        triton_poi_fused_convolution_relu_4[grid(7620608)](buf69,
            primals_39, 7620608, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_39
        buf70 = extern_kernels.convolution(buf69, primals_40, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 64, 122, 122), (952576, 14884, 122, 1))
        buf71 = empty_strided_cuda((244, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_29[grid(244)](buf71, 244, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf72 = empty_strided_cuda((244, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_30[grid(244)](buf72, 244, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf73 = empty_strided_cuda((244,), (1,), torch.int64)
        triton_poi_fused__to_copy_29[grid(244)](buf73, 244, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf74 = empty_strided_cuda((244,), (1,), torch.int64)
        triton_poi_fused_add_clamp_30[grid(244)](buf74, 244, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf75 = empty_strided_cuda((244,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_31[grid(244)](buf75,
            244, XBLOCK=256, num_warps=4, num_stages=1)
        buf77 = empty_strided_cuda((244, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_31[grid(244)](buf77,
            244, XBLOCK=256, num_warps=4, num_stages=1)
        buf76 = empty_strided_cuda((4, 64, 244, 244), (3811328, 59552, 244,
            1), torch.float32)
        buf78 = empty_strided_cuda((4, 64, 244, 244), (3811328, 59552, 244,
            1), torch.float32)
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_32[grid(
            15241216)](buf71, buf73, buf70, primals_41, buf74, buf75, buf72,
            buf77, buf76, buf78, 15241216, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf70
        del primals_41
        buf79 = empty_strided_cuda((4, 128, 256, 256), (8388608, 65536, 256,
            1), torch.float32)
        triton_poi_fused_cat_33[grid(33554432)](buf3, buf76, buf78, buf79, 
            33554432, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf76
        del buf78
        buf80 = extern_kernels.convolution(buf79, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf80, (4, 64, 254, 254), (4129024, 64516, 254, 1))
        buf81 = buf80
        del buf80
        triton_poi_fused_convolution_relu_0[grid(16516096)](buf81,
            primals_43, 16516096, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_43
        buf82 = extern_kernels.convolution(buf81, primals_44, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf82, (4, 64, 252, 252), (4064256, 63504, 252, 1))
        buf83 = empty_strided_cuda((4, 64, 256, 256), (4194304, 65536, 256,
            1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_relu_34[grid(16777216)](
            buf82, primals_45, buf83, 16777216, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf84 = extern_kernels.convolution(buf83, primals_46, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf84, (4, 4, 256, 256), (262144, 65536, 256, 1))
        buf85 = buf84
        del buf84
        triton_poi_fused_convolution_35[grid(1048576)](buf85, primals_47, 
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_47
        buf86 = empty_strided_cuda((4, 64, 252, 252), (4071424, 63616, 252,
            1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_36[grid(16257024)
            ](buf82, primals_45, buf86, 16257024, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf82
        del primals_45
    return (buf85, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, primals_22, primals_24, primals_26, primals_28,
        primals_30, primals_32, primals_34, primals_36, primals_38,
        primals_40, primals_42, primals_44, primals_46, buf1, buf3, buf4,
        buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17, buf19,
        buf21, buf22, buf23, buf25, buf27, buf29, buf30, buf31, buf32,
        buf33, buf35, buf37, buf39, buf41, buf43, buf44, buf45, buf46,
        buf47, buf49, buf51, buf53, buf55, buf57, buf58, buf59, buf60,
        buf61, buf63, buf65, buf67, buf69, buf71, buf72, buf73, buf74,
        buf75, buf77, buf79, buf81, buf83, buf86)


class DoubleConv(nn.Module):
    """
    Double 3x3 conv + relu
    """

    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv_1 = nn.Conv2d(in_channels, out_channels, 3)
        self.conv_2 = nn.Conv2d(out_channels, out_channels, 3)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv_1(x)
        x = self.relu(x)
        x = self.conv_2(x)
        x = self.relu(x)
        return x


class UpsampleCat(nn.Module):
    """
    Unsample input and concat with contracting tensor
    """

    def __init__(self, ch):
        super(UpsampleCat, self).__init__()
        self.up_conv = nn.Conv2d(ch, ch // 2, 3, padding=1)
        self.up = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)

    def forward(self, up, down):
        up = self.up_conv(up)
        up = self.up(up)
        up_w, up_h = up.size()[2:4]
        down_w, down_h = down.size()[2:4]
        dw = down_w + 4 - up_w
        dh = down_h + 4 - up_h
        down = F.pad(down, (2, 2, 2, 2))
        up = F.pad(up, (dw // 2, dw - dw // 2, dh // 2, dh - dh // 2))
        y = torch.cat([down, up], dim=1)
        return y


class UNetNew(nn.Module):
    """
    UNet model
    """

    def __init__(self, in_channels, out_channels):
        super(UNetNew, self).__init__()
        self.conv_1 = DoubleConv(in_channels, 64)
        self.conv_2 = DoubleConv(64, 128)
        self.conv_3 = DoubleConv(128, 256)
        self.conv_4 = DoubleConv(256, 512)
        self.conv_5 = DoubleConv(512, 1024)
        self.down = nn.MaxPool2d(2)
        self.up_1 = UpsampleCat(1024)
        self.up_2 = UpsampleCat(512)
        self.up_3 = UpsampleCat(256)
        self.up_4 = UpsampleCat(128)
        self.conv_6 = DoubleConv(1024, 512)
        self.conv_7 = DoubleConv(512, 256)
        self.conv_8 = DoubleConv(256, 128)
        self.conv_9 = DoubleConv(128, 64)
        self.out_conv = nn.Conv2d(64, out_channels, 1)

    def forward(self, input_0):
        primals_1 = self.conv_1.conv_1.weight
        primals_2 = self.conv_1.conv_1.bias
        primals_4 = self.conv_1.conv_2.weight
        primals_5 = self.conv_1.conv_2.bias
        primals_6 = self.conv_2.conv_1.weight
        primals_7 = self.conv_2.conv_1.bias
        primals_8 = self.conv_2.conv_2.weight
        primals_9 = self.conv_2.conv_2.bias
        primals_10 = self.conv_3.conv_1.weight
        primals_11 = self.conv_3.conv_1.bias
        primals_12 = self.conv_3.conv_2.weight
        primals_13 = self.conv_3.conv_2.bias
        primals_14 = self.conv_4.conv_1.weight
        primals_15 = self.conv_4.conv_1.bias
        primals_16 = self.conv_4.conv_2.weight
        primals_17 = self.conv_4.conv_2.bias
        primals_18 = self.conv_5.conv_1.weight
        primals_19 = self.conv_5.conv_1.bias
        primals_20 = self.conv_5.conv_2.weight
        primals_21 = self.conv_5.conv_2.bias
        primals_22 = self.up_1.up_conv.weight
        primals_23 = self.up_1.up_conv.bias
        primals_28 = self.up_2.up_conv.weight
        primals_29 = self.up_2.up_conv.bias
        primals_34 = self.up_3.up_conv.weight
        primals_35 = self.up_3.up_conv.bias
        primals_40 = self.up_4.up_conv.weight
        primals_41 = self.up_4.up_conv.bias
        primals_24 = self.conv_6.conv_1.weight
        primals_25 = self.conv_6.conv_1.bias
        primals_26 = self.conv_6.conv_2.weight
        primals_27 = self.conv_6.conv_2.bias
        primals_30 = self.conv_7.conv_1.weight
        primals_31 = self.conv_7.conv_1.bias
        primals_32 = self.conv_7.conv_2.weight
        primals_33 = self.conv_7.conv_2.bias
        primals_36 = self.conv_8.conv_1.weight
        primals_37 = self.conv_8.conv_1.bias
        primals_38 = self.conv_8.conv_2.weight
        primals_39 = self.conv_8.conv_2.bias
        primals_42 = self.conv_9.conv_1.weight
        primals_43 = self.conv_9.conv_1.bias
        primals_44 = self.conv_9.conv_2.weight
        primals_45 = self.conv_9.conv_2.bias
        primals_46 = self.out_conv.weight
        primals_47 = self.out_conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47])
        return output[0]
