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

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


# kernel path: runs/run_shard_7/inductor_cache/oi/coi4geilgwlqjngeai6hg4iruvpzcrvvpmho77vuyz3ppbade2sa.py
# Topologically Sorted Source Nodes: [conv2d, fx], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   fx => 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_7/inductor_cache/7o/c7ohemjzrjpmo5tghcbldpg3s3xexqjehsqi5j3see7mnjgihu4y.py
# Topologically Sorted Source Nodes: [conv2d_1, fx_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   fx_1 => 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_7/inductor_cache/xi/cxil7wfmjnrkoslosnjnwmoqebyussbsklzthwtzwxz2o74l47gs.py
# Topologically Sorted Source Nodes: [dp1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   dp1 => 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_7/inductor_cache/mo/cmozurfjru5huadwzrh5yjoitgoxelkjngdyjsgtehbyvs24isqk.py
# Topologically Sorted Source Nodes: [conv2d_2, fx_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   fx_2 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [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_7/inductor_cache/7z/c7zdlcnndhik4fhrd3ma2afaitw5ju2jqj5dhl26ipgwz2hgiher.py
# Topologically Sorted Source Nodes: [conv2d_3, fx_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   fx_3 => 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_7/inductor_cache/bb/cbbeqfykl2wgg3c65cet5cy67zimq7orfpepzs3vr2rlhu74t6er.py
# Topologically Sorted Source Nodes: [dp2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   dp2 => 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_7/inductor_cache/al/cale6iof6zxwoy6xmrfdcnforqmwzzvzi3cb4g6s4jjxqw3hb7np.py
# Topologically Sorted Source Nodes: [conv2d_4, fx_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   fx_4 => 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_7/inductor_cache/o2/co2av5pccpjevh6yky7hhdrsglymtzut2q3artudzrezlirfrzcg.py
# Topologically Sorted Source Nodes: [conv2d_5, fx_5], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
#   fx_5 => 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_7/inductor_cache/kt/cktc65bbrriywtquenbdrltvs6witq3gtcn3rvhoj2hholh5ihhi.py
# Topologically Sorted Source Nodes: [dp3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   dp3 => 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_7/inductor_cache/hi/chiqbenmv4ahnhr5zzytacsmj2bam534skwucufjcoevmj65liid.py
# Topologically Sorted Source Nodes: [conv2d_6, fx_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   fx_6 => 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_7/inductor_cache/4n/c4ng7b3xlqdmz4wf6padqdaqd63cmlob5sujzf4en6rjcglhiane.py
# Topologically Sorted Source Nodes: [conv2d_7, fx_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_7 => convolution_7
#   fx_7 => 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_7/inductor_cache/35/c356cd7zgpicezkybpa4roi4bdkpka2o2mqfytmksv4niqhdjpg5.py
# Topologically Sorted Source Nodes: [dp4], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   dp4 => 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_7/inductor_cache/h4/ch4ssn455dn4jzxavkj54g5nkxgb7fualnfv6kxozp5tbwrysclf.py
# Topologically Sorted Source Nodes: [conv2d_8, fx_8], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_8 => convolution_8
#   fx_8 => 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_7/inductor_cache/a6/ca64ahiz65uft4hynqujipch3z42hc5s7w664wtkg7x4xzxzq6so.py
# Topologically Sorted Source Nodes: [up], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up => 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_13 = async_compile.triton('triton_poi_fused__to_copy_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_13(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.4666666666666667
    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_7/inductor_cache/et/cet776q37zakeba3tsry2xhvahy4ivrxlfbh4hp6b3qw2h7hhpp7.py
# Topologically Sorted Source Nodes: [up], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up => 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_14 = async_compile.triton('triton_poi_fused_add_clamp_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_14(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.4666666666666667
    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_7/inductor_cache/ec/cectqgg5dx2kdtvvretcln2ko4ud6wti6zlftqt5neppb2z6e24m.py
# Topologically Sorted Source Nodes: [up], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up => 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_15 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_15(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.4666666666666667
    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_7/inductor_cache/u7/cu7vqlyzuplyitjbpira35mg2ztmlgiwciytlvkmqvoumfz4uudu.py
# Topologically Sorted Source Nodes: [conv2d_9, fx_9, up], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_9 => convolution_9
#   fx_9 => relu_9
#   up => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_3, add_4, mul_2, mul_3, mul_4, sub_1, sub_2, sub_4
# 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=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [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 = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_4), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x6 = (xindex // 256)
    x2 = (xindex // 256) % 1024
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (8*tmp4) + (64*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (8*tmp28) + (64*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (8*tmp28) + (64*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q3/cq3njclfukzz5cvkjglwh7tfty3ykzaraa6nzo6ukynvrgadrtka.py
# Topologically Sorted Source Nodes: [fx_10], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   fx_10 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_10, %slice_4], 1), kwargs = {})
triton_poi_fused_cat_17 = async_compile.triton('triton_poi_fused_cat_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_17', '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_17(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 256) % 1024
    x3 = (xindex // 262144)
    x4 = xindex % 256
    x0 = xindex % 16
    x1 = (xindex // 16) % 16
    x5 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + (256*x2) + (131072*x3)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 1024, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (100 + x0 + (24*x1) + (576*((-512) + x2)) + (294912*x3)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kd/ckdogzlgz4vukoqbkfl3hbv3vtpuekuse5h6gqr2qhfl5lcqxita.py
# Topologically Sorted Source Nodes: [conv2d_11, fx_11], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   fx_11 => relu_10
# Graph fragment:
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
triton_poi_fused_convolution_relu_18 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_18', '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_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 401408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 196) % 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_7/inductor_cache/55/c553vhqjrrmxc4unkjfbear4kgcvn3n3xvie75g4macymhgvs6fu.py
# Topologically Sorted Source Nodes: [up_1], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_1 => 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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), 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 = 24
    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 = 0.4782608695652174
    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_7/inductor_cache/j3/cj3ju4m5zz7zdte76upanjev6hdegkfdvrekkj2sqyjvhvzdgldh.py
# Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_1 => 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, 11), 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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), 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 = 24
    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 = 0.4782608695652174
    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], 11, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jz/cjzlyy4oqhsvivkt5s7ktuuaylp7lqimwwgu4zbk2a7ddbrqkr7g.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_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 = (24,), 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, 0.4782608695652174), 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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), 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 = 24
    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 = 0.4782608695652174
    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_7/inductor_cache/j6/cj6yyqvpb23ruaa7ftljnq6yxktlyqimfn3xlgavmfp4uhw6rpdt.py
# Topologically Sorted Source Nodes: [conv2d_12, fx_12, up_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   fx_12 => relu_11
#   up_1 => _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, add_7, add_8, add_9, mul_7, mul_8, mul_9, sub_6, sub_7, sub_9
# Graph fragment:
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_10, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_11 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %convert_element_type_5, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %convert_element_type_5, %clamp_max_5]), kwargs = {})
#   %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %clamp_max_4, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [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 = {})
#   %add_9 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %mul_9), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    x1 = (xindex // 24) % 24
    x0 = xindex % 24
    x6 = (xindex // 576)
    x2 = (xindex // 576) % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 12, 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 + (12*tmp4) + (144*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (12*tmp4) + (144*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (12*tmp28) + (144*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (12*tmp28) + (144*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/e7/ce7zct6mlnit2sfgyom6ga546krru5bo2oruwdli2d4tu4gy3ndx.py
# Topologically Sorted Source Nodes: [fx_13], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   fx_13 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_13, %slice_8], 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=[2097152], 
    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 = 1179648
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 576) % 512
    x3 = (xindex // 294912)
    x4 = xindex % 576
    x0 = xindex % 24
    x1 = (xindex // 24) % 24
    x5 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + (576*x2) + (147456*x3)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 512, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (928 + x0 + (57*x1) + (3249*((-256) + x2)) + (831744*x3)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/u6/cu6masetbxljk2hggensxzdbxbjks2alljpvl3oysltqtfyblmhu.py
# Topologically Sorted Source Nodes: [conv2d_14, fx_14], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_14 => convolution_14
#   fx_14 => relu_12
# Graph fragment:
#   %convolution_14 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_30, %primals_31, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_14,), kwargs = {})
triton_poi_fused_convolution_relu_24 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_24', '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_24(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 495616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 484) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gw/cgwtvkjnqkxeomcsiv4e2d5gqtio4mttxsjq4qkuclokhxtcbgay.py
# Topologically Sorted Source Nodes: [up_2], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_2 => 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_25 = async_compile.triton('triton_poi_fused__to_copy_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=[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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_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__to_copy_25(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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_7/inductor_cache/em/cemjv35sdirk2pjfrltzgwsargi3ril34jxadi5374ppapnmn2dr.py
# Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_2 => 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, 19), kwargs = {})
triton_poi_fused_add_clamp_26 = async_compile.triton('triton_poi_fused_add_clamp_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=[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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_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_add_clamp_26(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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], 19, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/cv/ccv45w4wtl7ugajp46jpdegg2h2f77qasyrvkkp4777bmngclese.py
# Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_2 => 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 = (40,), 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, 0.48717948717948717), 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_27 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_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=[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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_27', '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_27(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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_7/inductor_cache/u5/cu5p52jndijmnhg32h3eutm4emzfd75qfqi5yqrgwufvylhgrh6j.py
# Topologically Sorted Source Nodes: [conv2d_15, fx_15, up_2], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   fx_15 => relu_13
#   up_2 => _unsafe_index_10, _unsafe_index_11, _unsafe_index_8, _unsafe_index_9, add_12, add_13, add_14, mul_12, mul_13, mul_14, sub_11, sub_12, sub_14
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_12, %primals_32, %primals_33, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_13 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_15,), kwargs = {})
#   %_unsafe_index_8 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_13, [None, None, %convert_element_type_9, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_9 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_13, [None, None, %convert_element_type_9, %clamp_max_9]), kwargs = {})
#   %_unsafe_index_10 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_13, [None, None, %clamp_max_8, %convert_element_type_11]), kwargs = {})
#   %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_13, [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 = {})
#   %add_14 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_12, %mul_14), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1638400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 40) % 40
    x0 = xindex % 40
    x6 = (xindex // 1600)
    x2 = (xindex // 1600) % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 20, 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 + (20*tmp4) + (400*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (20*tmp4) + (400*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (20*tmp28) + (400*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (20*tmp28) + (400*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rm/crmgl5cf6uucmyeiysxy3r52xkfyqunt6pfmyxuunpoy7excvfj7.py
# Topologically Sorted Source Nodes: [fx_16], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   fx_16 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_16, %slice_12], 1), kwargs = {})
triton_poi_fused_cat_29 = async_compile.triton('triton_poi_fused_cat_29', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2097152], 
    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_29', '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_29(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1638400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 1600) % 256
    x3 = (xindex // 409600)
    x4 = xindex % 1600
    x0 = xindex % 40
    x1 = (xindex // 40) % 40
    x5 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + (1600*x2) + (204800*x3)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 256, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (5043 + x0 + (122*x1) + (14884*((-128) + x2)) + (1905152*x3)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ta/ctabnxczurmkwqgvxh4nmsbi7ma7ancz4ervpwcqk6saljh2rnfd.py
# Topologically Sorted Source Nodes: [conv2d_17, fx_17], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_17 => convolution_17
#   fx_17 => relu_14
# Graph fragment:
#   %convolution_17 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_36, %primals_37, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_14 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_17,), kwargs = {})
triton_poi_fused_convolution_relu_30 = async_compile.triton('triton_poi_fused_convolution_relu_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_30', '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_30(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 739328
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1444) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/z7/cz7xm7xpw754mbafv3n7ambb7ay2etrj2fjhgj76ia6eww7a4kll.py
# Topologically Sorted Source Nodes: [up_3], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   up_3 => 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_31 = async_compile.triton('triton_poi_fused__to_copy_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=[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_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_31(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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_7/inductor_cache/kz/ckzxdtp6i36wjwx62bmfa54boznpzhdphftyzkuvd5fhug3uzywb.py
# Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   up_3 => 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, 35), kwargs = {})
triton_poi_fused_add_clamp_32 = async_compile.triton('triton_poi_fused_add_clamp_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=[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_32', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_32(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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], 35, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hw/chwkrdrz74kbtwpzzed3iuj4mqvqob6qghhes6oz6ad7kwxdebsz.py
# Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   up_3 => 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 = (72,), 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, 0.49295774647887325), 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_33 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_33', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_33', '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_33(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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_7/inductor_cache/x2/cx2av3zghm5a6ndz5ybnpp5e4gj2pz6alycdvzuzbgtlknyce5zr.py
# Topologically Sorted Source Nodes: [conv2d_18, fx_18, up_3], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   fx_18 => relu_15
#   up_3 => _unsafe_index_12, _unsafe_index_13, _unsafe_index_14, _unsafe_index_15, add_17, add_18, add_19, mul_17, mul_18, mul_19, sub_16, sub_17, sub_19
# Graph fragment:
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_14, %primals_38, %primals_39, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_15 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_18,), kwargs = {})
#   %_unsafe_index_12 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_15, [None, None, %convert_element_type_13, %convert_element_type_15]), kwargs = {})
#   %_unsafe_index_13 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_15, [None, None, %convert_element_type_13, %clamp_max_13]), kwargs = {})
#   %_unsafe_index_14 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_15, [None, None, %clamp_max_12, %convert_element_type_15]), kwargs = {})
#   %_unsafe_index_15 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_15, [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 = {})
#   %add_19 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_17, %mul_19), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*i64', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2654208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 72) % 72
    x0 = xindex % 72
    x6 = (xindex // 5184)
    x2 = (xindex // 5184) % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 36, 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 + (36*tmp4) + (1296*x6)), None, eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + (36*tmp4) + (1296*x6)), None, eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + (36*tmp28) + (1296*x6)), None, eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + (36*tmp28) + (1296*x6)), None, eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/im/cimuburhw5i6cfvyetklpgcnecojkv2sage5wg62xajjpx2pa55o.py
# Topologically Sorted Source Nodes: [fx_19], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   fx_19 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_19, %slice_16], 1), kwargs = {})
triton_poi_fused_cat_35 = async_compile.triton('triton_poi_fused_cat_35', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_35', '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_35(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2654208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 5184) % 128
    x3 = (xindex // 663552)
    x4 = xindex % 5184
    x0 = xindex % 72
    x1 = (xindex // 72) % 72
    x5 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + (5184*x2) + (331776*x3)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 128, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (22770 + x0 + (252*x1) + (63504*((-64) + x2)) + (4064256*x3)), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + (x5), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tk/ctkmtqgf2iwtizbdnsne34kfdbgz3kl67uszncfg4x7seko6erf7.py
# Topologically Sorted Source Nodes: [conv2d_20, fx_20], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_20 => convolution_20
#   fx_20 => relu_16
# Graph fragment:
#   %convolution_20 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_42, %primals_43, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_16 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_20,), kwargs = {})
triton_poi_fused_convolution_relu_36 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_36', '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_36(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1254400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4900) % 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_7/inductor_cache/yi/cyi6ov2fxjddhtqklifei47tfp6ss7ak42vkd3exk6v5agm4s5x7.py
# Topologically Sorted Source Nodes: [conv2d_21, fx_21], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_21 => convolution_21
#   fx_21 => 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 = {})
triton_poi_fused_convolution_relu_37 = async_compile.triton('triton_poi_fused_convolution_relu_37', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_37', '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_37(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1183744
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4624) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/62/c623b3eox4t6yda6imsbrjjdy7ucnzqaibhlh6i2rjvc4zzd5bix.py
# Topologically Sorted Source Nodes: [fx_22], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   fx_22 => convolution_22
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_17, %primals_46, %primals_47, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_38 = async_compile.triton('triton_poi_fused_convolution_38', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_38', '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_38(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18496
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xw/cxwutd7zv5tp5m3juoax5fkmyir3ikg3mulmtzftdb4d3ihyoh4l.py
# Topologically Sorted Source Nodes: [conv2d_18, fx_18], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   fx_18 => relu_15
# Graph fragment:
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_14, %primals_38, %primals_39, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_15 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_18,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_15, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_39 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_39', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*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_39', '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_39(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 663552
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1296) % 128
    x0 = xindex % 1296
    x4 = (xindex // 1296)
    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 + (1408*x4)), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wl/cwlxy4tkzqsuxjmxsb3yx3nc2zm32cpwaklidvr4pf2hps6o4bd5.py
# Topologically Sorted Source Nodes: [conv2d_15, fx_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_15 => convolution_15
#   fx_15 => relu_13
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_12, %primals_32, %primals_33, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_13 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_15,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_13, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_40 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_40', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_40', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_40(in_ptr0, in_ptr1, out_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 // 400) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bk/cbkzyhr5mvcq4uxwaigxvg4okurxbhjk76xkchp2ukbgzfi2e54i.py
# Topologically Sorted Source Nodes: [conv2d_12, fx_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   fx_12 => relu_11
# Graph fragment:
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_10, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_11 : [num_users=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {})
#   %le_6 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_41 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_41', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_41', '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_41(in_ptr0, in_ptr1, out_ptr0, 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)
    x3 = xindex
    x1 = (xindex // 144) % 512
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sw/csw2cdzt4235jeuxw73ukyxitqrs2kep5rcqzr63fa43dlz6ubuo.py
# Topologically Sorted Source Nodes: [conv2d_9, fx_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_9 => convolution_9
#   fx_9 => 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=5] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
#   %le_8 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_42 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_42', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_42', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_42(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 1024
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 256, 256), (65536, 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, (1, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_47, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(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: [conv2d, fx], 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: [conv2d_1], 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: [conv2d_1, fx_1], 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: [dp1], 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: [conv2d_2], 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: [conv2d_2, fx_2], 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: [conv2d_3], 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: [conv2d_3, fx_3], 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: [dp2], 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: [conv2d_4], 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: [conv2d_4, fx_4], 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: [conv2d_5], 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: [conv2d_5, fx_5], 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: [dp3], 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: [conv2d_6], 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: [conv2d_6, fx_6], 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: [conv2d_7], 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: [conv2d_7, fx_7], 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: [dp4], 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: [conv2d_8], 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: [conv2d_8, fx_8], 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: [conv2d_9], 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 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_13.run(buf27, 16, grid=grid(16), stream=stream0)
        buf28 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_14.run(buf28, 16, grid=grid(16), stream=stream0)
        buf29 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_13.run(buf29, 16, grid=grid(16), stream=stream0)
        buf30 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_14.run(buf30, 16, grid=grid(16), stream=stream0)
        buf31 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_15.run(buf31, 16, grid=grid(16), stream=stream0)
        buf33 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_15.run(buf33, 16, grid=grid(16), stream=stream0)
        buf34 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1), torch.float32)
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, fx_9, up], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16.run(buf35, buf27, buf29, buf26, primals_21, buf30, buf31, buf28, buf33, 1048576, grid=grid(1048576), stream=stream0)
        # Topologically Sorted Source Nodes: [upconv], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf37 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fx_10], Original ATen: [aten.cat]
        triton_poi_fused_cat_17.run(buf36, primals_23, buf21, buf37, 1048576, grid=grid(1048576), stream=stream0)
        del buf36
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], 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, 14, 14), (100352, 196, 14, 1))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [conv2d_11, fx_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf39, primals_25, 401408, grid=grid(401408), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_12], 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, 12, 12), (73728, 144, 12, 1))
        buf41 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_19.run(buf41, 24, grid=grid(24), stream=stream0)
        buf42 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_20.run(buf42, 24, grid=grid(24), stream=stream0)
        buf43 = empty_strided_cuda((24, ), (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_19.run(buf43, 24, grid=grid(24), stream=stream0)
        buf44 = empty_strided_cuda((24, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_1], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_20.run(buf44, 24, grid=grid(24), stream=stream0)
        buf45 = empty_strided_cuda((24, ), (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_21.run(buf45, 24, grid=grid(24), stream=stream0)
        buf47 = empty_strided_cuda((24, 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_21.run(buf47, 24, grid=grid(24), stream=stream0)
        buf48 = empty_strided_cuda((4, 512, 24, 24), (294912, 576, 24, 1), torch.float32)
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [conv2d_12, fx_12, up_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22.run(buf49, buf41, buf43, buf40, primals_27, buf44, buf45, buf42, buf47, 1179648, grid=grid(1179648), stream=stream0)
        # Topologically Sorted Source Nodes: [upconv_1], Original ATen: [aten.convolution]
        buf50 = extern_kernels.convolution(buf49, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 256, 24, 24), (147456, 576, 24, 1))
        buf51 = empty_strided_cuda((4, 512, 24, 24), (294912, 576, 24, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fx_13], Original ATen: [aten.cat]
        triton_poi_fused_cat_23.run(buf50, primals_29, buf15, buf51, 1179648, grid=grid(1179648), stream=stream0)
        del buf50
        del primals_29
        # Topologically Sorted Source Nodes: [conv2d_14], 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, 22, 22), (123904, 484, 22, 1))
        buf53 = buf52; del buf52  # reuse
        # Topologically Sorted Source Nodes: [conv2d_14, fx_14], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_24.run(buf53, primals_31, 495616, grid=grid(495616), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [conv2d_15], 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, 20, 20), (102400, 400, 20, 1))
        buf55 = empty_strided_cuda((40, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_25.run(buf55, 40, grid=grid(40), stream=stream0)
        buf56 = empty_strided_cuda((40, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_26.run(buf56, 40, grid=grid(40), stream=stream0)
        buf57 = empty_strided_cuda((40, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_25.run(buf57, 40, grid=grid(40), stream=stream0)
        buf58 = empty_strided_cuda((40, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_26.run(buf58, 40, grid=grid(40), stream=stream0)
        buf59 = empty_strided_cuda((40, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_27.run(buf59, 40, grid=grid(40), stream=stream0)
        buf61 = empty_strided_cuda((40, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_2], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_27.run(buf61, 40, grid=grid(40), stream=stream0)
        buf62 = empty_strided_cuda((4, 256, 40, 40), (409600, 1600, 40, 1), torch.float32)
        buf63 = buf62; del buf62  # reuse
        # Topologically Sorted Source Nodes: [conv2d_15, fx_15, up_2], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28.run(buf63, buf55, buf57, buf54, primals_33, buf58, buf59, buf56, buf61, 1638400, grid=grid(1638400), stream=stream0)
        # Topologically Sorted Source Nodes: [upconv_2], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf63, primals_34, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 128, 40, 40), (204800, 1600, 40, 1))
        buf65 = empty_strided_cuda((4, 256, 40, 40), (409600, 1600, 40, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fx_16], Original ATen: [aten.cat]
        triton_poi_fused_cat_29.run(buf64, primals_35, buf9, buf65, 1638400, grid=grid(1638400), stream=stream0)
        del buf64
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_17], 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, 38, 38), (184832, 1444, 38, 1))
        buf67 = buf66; del buf66  # reuse
        # Topologically Sorted Source Nodes: [conv2d_17, fx_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_30.run(buf67, primals_37, 739328, grid=grid(739328), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [conv2d_18], 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, 36, 36), (165888, 1296, 36, 1))
        buf69 = empty_strided_cuda((72, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_31.run(buf69, 72, grid=grid(72), stream=stream0)
        buf70 = empty_strided_cuda((72, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_32.run(buf70, 72, grid=grid(72), stream=stream0)
        buf71 = empty_strided_cuda((72, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_31.run(buf71, 72, grid=grid(72), stream=stream0)
        buf72 = empty_strided_cuda((72, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_32.run(buf72, 72, grid=grid(72), stream=stream0)
        buf73 = empty_strided_cuda((72, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_33.run(buf73, 72, grid=grid(72), stream=stream0)
        buf75 = empty_strided_cuda((72, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [up_3], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_33.run(buf75, 72, grid=grid(72), stream=stream0)
        buf76 = empty_strided_cuda((4, 128, 72, 72), (663552, 5184, 72, 1), torch.float32)
        buf77 = buf76; del buf76  # reuse
        # Topologically Sorted Source Nodes: [conv2d_18, fx_18, up_3], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34.run(buf77, buf69, buf71, buf68, primals_39, buf72, buf73, buf70, buf75, 2654208, grid=grid(2654208), stream=stream0)
        # Topologically Sorted Source Nodes: [upconv_3], Original ATen: [aten.convolution]
        buf78 = extern_kernels.convolution(buf77, primals_40, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 64, 72, 72), (331776, 5184, 72, 1))
        buf79 = empty_strided_cuda((4, 128, 72, 72), (663552, 5184, 72, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fx_19], Original ATen: [aten.cat]
        triton_poi_fused_cat_35.run(buf78, primals_41, buf3, buf79, 2654208, grid=grid(2654208), stream=stream0)
        del buf78
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], 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, 70, 70), (313600, 4900, 70, 1))
        buf81 = buf80; del buf80  # reuse
        # Topologically Sorted Source Nodes: [conv2d_20, fx_20], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_36.run(buf81, primals_43, 1254400, grid=grid(1254400), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [conv2d_21], 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, 68, 68), (295936, 4624, 68, 1))
        buf83 = buf82; del buf82  # reuse
        # Topologically Sorted Source Nodes: [conv2d_21, fx_21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_37.run(buf83, primals_45, 1183744, grid=grid(1183744), stream=stream0)
        del primals_45
        # Topologically Sorted Source Nodes: [fx_22], 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, 1, 68, 68), (4624, 4624, 68, 1))
        buf85 = buf84; del buf84  # reuse
        # Topologically Sorted Source Nodes: [fx_22], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_38.run(buf85, primals_47, 18496, grid=grid(18496), stream=stream0)
        del primals_47
        buf86 = empty_strided_cuda((4, 128, 36, 36), (180224, 1408, 36, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_18, fx_18], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_39.run(buf68, primals_39, buf86, 663552, grid=grid(663552), stream=stream0)
        del buf68
        del primals_39
        buf87 = empty_strided_cuda((4, 256, 20, 20), (102400, 400, 20, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_15, fx_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_40.run(buf54, primals_33, buf87, 409600, grid=grid(409600), stream=stream0)
        del buf54
        del primals_33
        buf88 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_12, fx_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_41.run(buf40, primals_27, buf88, 294912, grid=grid(294912), stream=stream0)
        del buf40
        del primals_27
        buf89 = empty_strided_cuda((4, 1024, 8, 8), (65536, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_9, fx_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_42.run(buf26, primals_21, buf89, 262144, grid=grid(262144), stream=stream0)
        del buf26
        del primals_21
    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, buf28, buf29, buf30, buf31, buf33, buf35, buf37, buf39, buf41, buf42, buf43, buf44, buf45, buf47, buf49, buf51, buf53, buf55, buf56, buf57, buf58, buf59, buf61, buf63, buf65, buf67, buf69, buf70, buf71, buf72, buf73, buf75, buf77, buf79, buf81, buf83, buf86, buf87, buf88, buf89, )


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


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

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


class ContractingBlock(nn.Module):
    """
    ContractingBlock Class
    Performs two convolutions followed by a max pool operation.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv3x3_0 = nn.Conv2d(in_channels, out_channels, kernel_size=3)
        self.conv3x3_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3)

    def forward(self, x):
        """
        Function for completing a forward pass of ContractingBlock: 
        Given an image tensor, completes a contracting block and returns the transformed tensor.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
        """
        fx = F.relu(self.conv3x3_0(x))
        fx = F.relu(self.conv3x3_1(fx))
        return fx


class ExpandingBlock(nn.Module):
    """
    ExpandingBlock
    Performs an upsampling, a convolution, a concatenation of its two inputs,
    followed by two more convolutions.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, hid_channels):
        super(ExpandingBlock, self).__init__()
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)
        self.conv = nn.Conv2d(hid_channels, hid_channels // 2, kernel_size=
            3, stride=1, padding=1)
        self.conv3x3_0 = nn.Conv2d(hid_channels, hid_channels // 2,
            kernel_size=3, stride=1)
        self.conv3x3_1 = nn.Conv2d(hid_channels // 2, hid_channels // 2,
            kernel_size=3, stride=1)

    @staticmethod
    def crop(x, shape=None):
        """
        Function for cropping an image tensor: Given an image tensor and the new shape,
        crops to the center pixels (assumes that the input's size and the new size are
        even numbers).
        Parameters:
            image: image tensor of shape (batch size, channels, height, width)
            new_shape: a torch.Size object with the shape you want x to have
        """
        _, _, h, w = x.shape
        _, _, h_new, w_new = shape
        ch, cw = h // 2, w // 2
        ch_new, cw_new = h_new // 2, w_new // 2
        x1 = int(cw - cw_new)
        y1 = int(ch - ch_new)
        x2 = int(x1 + w_new)
        y2 = int(y1 + h_new)
        return x[:, :, y1:y2, x1:x2]

    def forward(self, x, skip):
        """
        Function for completing a forward pass of ExpandingBlock: 
        Given an image tensor, completes an expanding block and returns the transformed tensor.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
            skip_con_x: the image tensor from the contracting path (from the opposing block of x)
                    for the skip connection
        """
        up = self.upsample(x)
        upconv = self.conv(up)
        skip = self.crop(skip, upconv.shape)
        fx = torch.cat([upconv, skip], dim=1)
        fx = F.relu(self.conv3x3_0(fx))
        fx = F.relu(self.conv3x3_1(fx))
        return fx


class FeatureMapBlock(nn.Module):
    """
    FeatureMapBlock
    The final layer of a UNet - 
    maps each pixel to a pixel with the correct number of output dimensions
    using a 1x1 convolution.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, in_channels, out_channels):
        super(FeatureMapBlock, self).__init__()
        self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        """
        Function for completing a forward pass of FeatureMapBlock: 
        Given an image tensor, returns it mapped to the desired number of channels.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
        """
        fx = self.conv1x1(x)
        return fx


class Unet(nn.Module):
    """
    UNet Class
    A series of 4 contracting blocks followed by 4 expanding blocks to 
    transform an input image into the corresponding paired image, with an upfeature
    layer at the start and a downfeature layer at the end
    Values:
        input_channels: the number of channels to expect from a given input
        output_channels: the number of channels to expect for a given output
    """

    def __init__(self, in_channels=1, hid_channels=64, out_channels=1):
        super(Unet, self).__init__()
        self.contract1 = ContractingBlock(1, hid_channels)
        self.contract2 = ContractingBlock(hid_channels, hid_channels * 2)
        self.contract3 = ContractingBlock(hid_channels * 2, hid_channels * 4)
        self.contract4 = ContractingBlock(hid_channels * 4, hid_channels * 8)
        self.bottleneck = ContractingBlock(hid_channels * 8, hid_channels * 16)
        self.expand1 = ExpandingBlock(hid_channels * 16)
        self.expand2 = ExpandingBlock(hid_channels * 8)
        self.expand3 = ExpandingBlock(hid_channels * 4)
        self.expand4 = ExpandingBlock(hid_channels * 2)
        self.downfeature = FeatureMapBlock(hid_channels, out_channels)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        """
        Function for completing a forward pass of UNet: 
        Given an image tensor, passes it through U-Net and returns the output.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
        """
        d1 = self.contract1(x)
        dp1 = self.pool(d1)
        d2 = self.contract2(dp1)
        dp2 = self.pool(d2)
        d3 = self.contract3(dp2)
        dp3 = self.pool(d3)
        d4 = self.contract4(dp3)
        dp4 = self.pool(d4)
        b = self.bottleneck(dp4)
        up1 = self.expand1(b, d4)
        up2 = self.expand2(up1, d3)
        up3 = self.expand3(up2, d2)
        up4 = self.expand4(up3, d1)
        xn = self.downfeature(up4)
        return xn


def get_inputs():
    return [torch.rand([4, 1, 256, 256])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torch.nn import 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__to_copy_13(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.4666666666666667
    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_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)
    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_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 = 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_relu_sub_16(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x6 = xindex // 256
    x2 = xindex // 256 % 1024
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 8 * tmp4 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 8 * tmp28 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 8 * tmp28 + 64 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_cat_17(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 // 256 % 1024
    x3 = xindex // 262144
    x4 = xindex % 256
    x0 = xindex % 16
    x1 = xindex // 16 % 16
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 256 * x2 + 131072 * x3), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 1024, tl.int64)
    tmp13 = tl.load(in_ptr2 + (100 + x0 + 24 * x1 + 576 * (-512 + x2) + 
        294912 * x3), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_relu_18(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 // 196 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_19(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 24
    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 = 0.4782608695652174
    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 = 24
    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 = 0.4782608695652174
    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], 11, 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 = 24
    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 = 0.4782608695652174
    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_relu_sub_22(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 24 % 24
    x0 = xindex % 24
    x6 = xindex // 576
    x2 = xindex // 576 % 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 12, 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 + 12 * tmp4 + 144 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 12 * tmp4 + 144 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 12 * tmp28 + 144 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 12 * tmp28 + 144 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_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 // 576 % 512
    x3 = xindex // 294912
    x4 = xindex % 576
    x0 = xindex % 24
    x1 = xindex // 24 % 24
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 576 * x2 + 147456 * x3), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp13 = tl.load(in_ptr2 + (928 + x0 + 57 * x1 + 3249 * (-256 + x2) + 
        831744 * x3), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_relu_24(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 // 484 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused__to_copy_25(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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_26(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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], 19, 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_27(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 40
    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 = 0.48717948717948717
    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_relu_sub_28(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 40 % 40
    x0 = xindex % 40
    x6 = xindex // 1600
    x2 = xindex // 1600 % 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 20, 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 + 20 * tmp4 + 400 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 20 * tmp4 + 400 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 20 * tmp28 + 400 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 20 * tmp28 + 400 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_cat_29(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 // 1600 % 256
    x3 = xindex // 409600
    x4 = xindex % 1600
    x0 = xindex % 40
    x1 = xindex // 40 % 40
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 1600 * x2 + 204800 * x3), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp13 = tl.load(in_ptr2 + (5043 + x0 + 122 * x1 + 14884 * (-128 + x2) +
        1905152 * x3), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_relu_30(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 // 1444 % 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__to_copy_31(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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_32(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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], 35, 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_33(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 72
    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 = 0.49295774647887325
    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_relu_sub_34(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 72 % 72
    x0 = xindex % 72
    x6 = xindex // 5184
    x2 = xindex // 5184 % 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 36, 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 + 36 * tmp4 + 1296 * x6), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = tmp14 + tmp1
    tmp16 = tmp14 < 0
    tmp17 = tl.where(tmp16, tmp15, tmp14)
    tmp18 = tl.load(in_ptr2 + (tmp17 + 36 * tmp4 + 1296 * x6), None,
        eviction_policy='evict_last')
    tmp19 = tmp18 + tmp10
    tmp20 = triton_helpers.maximum(tmp12, tmp19)
    tmp21 = tmp20 - tmp13
    tmp23 = tmp21 * tmp22
    tmp24 = tmp13 + tmp23
    tmp26 = tmp25 + tmp1
    tmp27 = tmp25 < 0
    tmp28 = tl.where(tmp27, tmp26, tmp25)
    tmp29 = tl.load(in_ptr2 + (tmp8 + 36 * tmp28 + 1296 * x6), None,
        eviction_policy='evict_last')
    tmp30 = tmp29 + tmp10
    tmp31 = triton_helpers.maximum(tmp12, tmp30)
    tmp32 = tl.load(in_ptr2 + (tmp17 + 36 * tmp28 + 1296 * x6), None,
        eviction_policy='evict_last')
    tmp33 = tmp32 + tmp10
    tmp34 = triton_helpers.maximum(tmp12, tmp33)
    tmp35 = tmp34 - tmp31
    tmp36 = tmp35 * tmp22
    tmp37 = tmp31 + tmp36
    tmp38 = tmp37 - tmp24
    tmp40 = tmp38 * tmp39
    tmp41 = tmp24 + tmp40
    tl.store(in_out_ptr0 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_cat_35(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 // 5184 % 128
    x3 = xindex // 663552
    x4 = xindex % 5184
    x0 = xindex % 72
    x1 = xindex // 72 % 72
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 5184 * x2 + 331776 * x3), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp13 = tl.load(in_ptr2 + (22770 + x0 + 252 * x1 + 63504 * (-64 + x2) +
        4064256 * x3), tmp10, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_relu_36(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1254400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4900 % 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_37(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 // 4624 % 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_convolution_38(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 18496
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_39(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 // 1296 % 128
    x0 = xindex % 1296
    x4 = xindex // 1296
    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 + 1408 * x4), tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_40(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 400 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_41(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 // 144 % 512
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_42(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 1024
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47) = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 256, 256), (65536, 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, (1, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_47, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(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=512, num_warps=8, 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=1024, num_warps=4, 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=1024, num_warps=4, 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=1024, num_warps=4, 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 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_13[grid(16)](buf27, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf28 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_14[grid(16)](buf28, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf29 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_13[grid(16)](buf29, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf30 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_add_clamp_14[grid(16)](buf30, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf31 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_15[grid(16)](buf31, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf33 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_15[grid(16)](buf33, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf34 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1),
            torch.float32)
        buf35 = buf34
        del buf34
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_16[grid
            (1048576)](buf35, buf27, buf29, buf26, primals_21, buf30, buf31,
            buf28, buf33, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        buf36 = extern_kernels.convolution(buf35, primals_22, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 512, 16, 16), (131072, 256, 16, 1))
        buf37 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1),
            torch.float32)
        triton_poi_fused_cat_17[grid(1048576)](buf36, primals_23, buf21,
            buf37, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf36
        del primals_23
        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, 14, 14), (100352, 196, 14, 1))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_relu_18[grid(401408)](buf39,
            primals_25, 401408, XBLOCK=512, num_warps=8, 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, 12, 12), (73728, 144, 12, 1))
        buf41 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_19[grid(24)](buf41, 24, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf42 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_20[grid(24)](buf42, 24, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf43 = empty_strided_cuda((24,), (1,), torch.int64)
        triton_poi_fused__to_copy_19[grid(24)](buf43, 24, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf44 = empty_strided_cuda((24,), (1,), torch.int64)
        triton_poi_fused_add_clamp_20[grid(24)](buf44, 24, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf45 = empty_strided_cuda((24,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(24)](buf45, 
            24, XBLOCK=32, num_warps=1, num_stages=1)
        buf47 = empty_strided_cuda((24, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(24)](buf47, 
            24, XBLOCK=32, num_warps=1, num_stages=1)
        buf48 = empty_strided_cuda((4, 512, 24, 24), (294912, 576, 24, 1),
            torch.float32)
        buf49 = buf48
        del buf48
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22[grid
            (1179648)](buf49, buf41, buf43, buf40, primals_27, buf44, buf45,
            buf42, buf47, 1179648, XBLOCK=1024, num_warps=4, num_stages=1)
        buf50 = extern_kernels.convolution(buf49, primals_28, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 256, 24, 24), (147456, 576, 24, 1))
        buf51 = empty_strided_cuda((4, 512, 24, 24), (294912, 576, 24, 1),
            torch.float32)
        triton_poi_fused_cat_23[grid(1179648)](buf50, primals_29, buf15,
            buf51, 1179648, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf50
        del primals_29
        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, 22, 22), (123904, 484, 22, 1))
        buf53 = buf52
        del buf52
        triton_poi_fused_convolution_relu_24[grid(495616)](buf53,
            primals_31, 495616, 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, 20, 20), (102400, 400, 20, 1))
        buf55 = empty_strided_cuda((40, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_25[grid(40)](buf55, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf56 = empty_strided_cuda((40, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_26[grid(40)](buf56, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf57 = empty_strided_cuda((40,), (1,), torch.int64)
        triton_poi_fused__to_copy_25[grid(40)](buf57, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf58 = empty_strided_cuda((40,), (1,), torch.int64)
        triton_poi_fused_add_clamp_26[grid(40)](buf58, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf59 = empty_strided_cuda((40,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_27[grid(40)](buf59, 
            40, XBLOCK=64, num_warps=1, num_stages=1)
        buf61 = empty_strided_cuda((40, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_27[grid(40)](buf61, 
            40, XBLOCK=64, num_warps=1, num_stages=1)
        buf62 = empty_strided_cuda((4, 256, 40, 40), (409600, 1600, 40, 1),
            torch.float32)
        buf63 = buf62
        del buf62
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28[grid
            (1638400)](buf63, buf55, buf57, buf54, primals_33, buf58, buf59,
            buf56, buf61, 1638400, XBLOCK=1024, num_warps=4, num_stages=1)
        buf64 = extern_kernels.convolution(buf63, primals_34, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 128, 40, 40), (204800, 1600, 40, 1))
        buf65 = empty_strided_cuda((4, 256, 40, 40), (409600, 1600, 40, 1),
            torch.float32)
        triton_poi_fused_cat_29[grid(1638400)](buf64, primals_35, buf9,
            buf65, 1638400, XBLOCK=512, num_warps=8, num_stages=1)
        del buf64
        del primals_35
        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, 38, 38), (184832, 1444, 38, 1))
        buf67 = buf66
        del buf66
        triton_poi_fused_convolution_relu_30[grid(739328)](buf67,
            primals_37, 739328, 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, 36, 36), (165888, 1296, 36, 1))
        buf69 = empty_strided_cuda((72, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_31[grid(72)](buf69, 72, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf70 = empty_strided_cuda((72, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_32[grid(72)](buf70, 72, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf71 = empty_strided_cuda((72,), (1,), torch.int64)
        triton_poi_fused__to_copy_31[grid(72)](buf71, 72, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf72 = empty_strided_cuda((72,), (1,), torch.int64)
        triton_poi_fused_add_clamp_32[grid(72)](buf72, 72, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf73 = empty_strided_cuda((72,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_33[grid(72)](buf73, 
            72, XBLOCK=128, num_warps=4, num_stages=1)
        buf75 = empty_strided_cuda((72, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_33[grid(72)](buf75, 
            72, XBLOCK=128, num_warps=4, num_stages=1)
        buf76 = empty_strided_cuda((4, 128, 72, 72), (663552, 5184, 72, 1),
            torch.float32)
        buf77 = buf76
        del buf76
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34[grid
            (2654208)](buf77, buf69, buf71, buf68, primals_39, buf72, buf73,
            buf70, buf75, 2654208, XBLOCK=1024, num_warps=4, num_stages=1)
        buf78 = extern_kernels.convolution(buf77, primals_40, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 64, 72, 72), (331776, 5184, 72, 1))
        buf79 = empty_strided_cuda((4, 128, 72, 72), (663552, 5184, 72, 1),
            torch.float32)
        triton_poi_fused_cat_35[grid(2654208)](buf78, primals_41, buf3,
            buf79, 2654208, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf78
        del primals_41
        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, 70, 70), (313600, 4900, 70, 1))
        buf81 = buf80
        del buf80
        triton_poi_fused_convolution_relu_36[grid(1254400)](buf81,
            primals_43, 1254400, XBLOCK=1024, num_warps=4, 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, 68, 68), (295936, 4624, 68, 1))
        buf83 = buf82
        del buf82
        triton_poi_fused_convolution_relu_37[grid(1183744)](buf83,
            primals_45, 1183744, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_45
        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, 1, 68, 68), (4624, 4624, 68, 1))
        buf85 = buf84
        del buf84
        triton_poi_fused_convolution_38[grid(18496)](buf85, primals_47, 
            18496, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_47
        buf86 = empty_strided_cuda((4, 128, 36, 36), (180224, 1408, 36, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_39[grid(663552)](
            buf68, primals_39, buf86, 663552, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf68
        del primals_39
        buf87 = empty_strided_cuda((4, 256, 20, 20), (102400, 400, 20, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_40[grid(409600)](
            buf54, primals_33, buf87, 409600, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf54
        del primals_33
        buf88 = empty_strided_cuda((4, 512, 12, 12), (73728, 144, 12, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_41[grid(294912)](
            buf40, primals_27, buf88, 294912, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf40
        del primals_27
        buf89 = empty_strided_cuda((4, 1024, 8, 8), (65536, 64, 8, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_42[grid(262144)](
            buf26, primals_21, buf89, 262144, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf26
        del primals_21
    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, buf28, buf29, buf30, buf31,
        buf33, buf35, buf37, buf39, buf41, buf42, buf43, buf44, buf45,
        buf47, buf49, buf51, buf53, buf55, buf56, buf57, buf58, buf59,
        buf61, buf63, buf65, buf67, buf69, buf70, buf71, buf72, buf73,
        buf75, buf77, buf79, buf81, buf83, buf86, buf87, buf88, buf89)


class ContractingBlock(nn.Module):
    """
    ContractingBlock Class
    Performs two convolutions followed by a max pool operation.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv3x3_0 = nn.Conv2d(in_channels, out_channels, kernel_size=3)
        self.conv3x3_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3)

    def forward(self, x):
        """
        Function for completing a forward pass of ContractingBlock: 
        Given an image tensor, completes a contracting block and returns the transformed tensor.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
        """
        fx = F.relu(self.conv3x3_0(x))
        fx = F.relu(self.conv3x3_1(fx))
        return fx


class ExpandingBlock(nn.Module):
    """
    ExpandingBlock
    Performs an upsampling, a convolution, a concatenation of its two inputs,
    followed by two more convolutions.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, hid_channels):
        super(ExpandingBlock, self).__init__()
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)
        self.conv = nn.Conv2d(hid_channels, hid_channels // 2, kernel_size=
            3, stride=1, padding=1)
        self.conv3x3_0 = nn.Conv2d(hid_channels, hid_channels // 2,
            kernel_size=3, stride=1)
        self.conv3x3_1 = nn.Conv2d(hid_channels // 2, hid_channels // 2,
            kernel_size=3, stride=1)

    @staticmethod
    def crop(x, shape=None):
        """
        Function for cropping an image tensor: Given an image tensor and the new shape,
        crops to the center pixels (assumes that the input's size and the new size are
        even numbers).
        Parameters:
            image: image tensor of shape (batch size, channels, height, width)
            new_shape: a torch.Size object with the shape you want x to have
        """
        _, _, h, w = x.shape
        _, _, h_new, w_new = shape
        ch, cw = h // 2, w // 2
        ch_new, cw_new = h_new // 2, w_new // 2
        x1 = int(cw - cw_new)
        y1 = int(ch - ch_new)
        x2 = int(x1 + w_new)
        y2 = int(y1 + h_new)
        return x[:, :, y1:y2, x1:x2]

    def forward(self, x, skip):
        """
        Function for completing a forward pass of ExpandingBlock: 
        Given an image tensor, completes an expanding block and returns the transformed tensor.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
            skip_con_x: the image tensor from the contracting path (from the opposing block of x)
                    for the skip connection
        """
        up = self.upsample(x)
        upconv = self.conv(up)
        skip = self.crop(skip, upconv.shape)
        fx = torch.cat([upconv, skip], dim=1)
        fx = F.relu(self.conv3x3_0(fx))
        fx = F.relu(self.conv3x3_1(fx))
        return fx


class FeatureMapBlock(nn.Module):
    """
    FeatureMapBlock
    The final layer of a UNet - 
    maps each pixel to a pixel with the correct number of output dimensions
    using a 1x1 convolution.
    Values:
        input_channels: the number of channels to expect from a given input
    """

    def __init__(self, in_channels, out_channels):
        super(FeatureMapBlock, self).__init__()
        self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        """
        Function for completing a forward pass of FeatureMapBlock: 
        Given an image tensor, returns it mapped to the desired number of channels.
        Parameters:
            x: image tensor of shape (batch size, channels, height, width)
        """
        fx = self.conv1x1(x)
        return fx


class UnetNew(nn.Module):
    """
    UNet Class
    A series of 4 contracting blocks followed by 4 expanding blocks to 
    transform an input image into the corresponding paired image, with an upfeature
    layer at the start and a downfeature layer at the end
    Values:
        input_channels: the number of channels to expect from a given input
        output_channels: the number of channels to expect for a given output
    """

    def __init__(self, in_channels=1, hid_channels=64, out_channels=1):
        super(UnetNew, self).__init__()
        self.contract1 = ContractingBlock(1, hid_channels)
        self.contract2 = ContractingBlock(hid_channels, hid_channels * 2)
        self.contract3 = ContractingBlock(hid_channels * 2, hid_channels * 4)
        self.contract4 = ContractingBlock(hid_channels * 4, hid_channels * 8)
        self.bottleneck = ContractingBlock(hid_channels * 8, hid_channels * 16)
        self.expand1 = ExpandingBlock(hid_channels * 16)
        self.expand2 = ExpandingBlock(hid_channels * 8)
        self.expand3 = ExpandingBlock(hid_channels * 4)
        self.expand4 = ExpandingBlock(hid_channels * 2)
        self.downfeature = FeatureMapBlock(hid_channels, out_channels)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, input_0):
        primals_1 = self.contract1.conv3x3_0.weight
        primals_2 = self.contract1.conv3x3_0.bias
        primals_4 = self.contract1.conv3x3_1.weight
        primals_5 = self.contract1.conv3x3_1.bias
        primals_6 = self.contract2.conv3x3_0.weight
        primals_7 = self.contract2.conv3x3_0.bias
        primals_8 = self.contract2.conv3x3_1.weight
        primals_9 = self.contract2.conv3x3_1.bias
        primals_10 = self.contract3.conv3x3_0.weight
        primals_11 = self.contract3.conv3x3_0.bias
        primals_12 = self.contract3.conv3x3_1.weight
        primals_13 = self.contract3.conv3x3_1.bias
        primals_14 = self.contract4.conv3x3_0.weight
        primals_15 = self.contract4.conv3x3_0.bias
        primals_16 = self.contract4.conv3x3_1.weight
        primals_17 = self.contract4.conv3x3_1.bias
        primals_18 = self.bottleneck.conv3x3_0.weight
        primals_19 = self.bottleneck.conv3x3_0.bias
        primals_20 = self.bottleneck.conv3x3_1.weight
        primals_21 = self.bottleneck.conv3x3_1.bias
        primals_22 = self.expand1.conv.weight
        primals_23 = self.expand1.conv.bias
        primals_24 = self.expand1.conv3x3_0.weight
        primals_25 = self.expand1.conv3x3_0.bias
        primals_26 = self.expand1.conv3x3_1.weight
        primals_27 = self.expand1.conv3x3_1.bias
        primals_28 = self.expand2.conv.weight
        primals_29 = self.expand2.conv.bias
        primals_30 = self.expand2.conv3x3_0.weight
        primals_31 = self.expand2.conv3x3_0.bias
        primals_32 = self.expand2.conv3x3_1.weight
        primals_33 = self.expand2.conv3x3_1.bias
        primals_34 = self.expand3.conv.weight
        primals_35 = self.expand3.conv.bias
        primals_36 = self.expand3.conv3x3_0.weight
        primals_37 = self.expand3.conv3x3_0.bias
        primals_38 = self.expand3.conv3x3_1.weight
        primals_39 = self.expand3.conv3x3_1.bias
        primals_40 = self.expand4.conv.weight
        primals_41 = self.expand4.conv.bias
        primals_42 = self.expand4.conv3x3_0.weight
        primals_43 = self.expand4.conv3x3_0.bias
        primals_44 = self.expand4.conv3x3_1.weight
        primals_45 = self.expand4.conv3x3_1.bias
        primals_46 = self.downfeature.conv1x1.weight
        primals_47 = self.downfeature.conv1x1.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]
