# 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_6/inductor_cache/f7/cf7tayhctr3m6ezk7xezotpdlc5h4drokdkbz4vy2pfkbdxnmn4q.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[256, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 192
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (27*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5b/c5brnjme4e4oybuabwsko4vuljormwjqoawce7jgxo5fbkhzx55r.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[16, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (12288*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/xq/cxq75w43anllid5ys7ss3yyizuoeph3vvaqlvm5lo434hrywtyle.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_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=[4096, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/nw/cnwm6ljuusoqjcwr2jdx6p2ue7ldghxjdr3oe62stiuqhsboiczy.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_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=[8192, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8192
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/32/c32xiwptfqtyhbnde262mvq5tzywzo6zquurttkv7sztqnze6yni.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_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=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jj/cjjz4tpbucpuc3faa2ky32crfwhb5fbnssd6o2yfkgdcjg2acfmo.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 32768
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/tg/ctgdsxjd3rciejxtjvi3y2w5fmmggh5lm3mivuygvkdzeb3zulmc.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_6 = async_compile.triton('triton_poi_fused_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/e7/ce7jqsdrj5poslb2hpufqd2wdux5xiab5n2auqal3ztzvkzrmnzl.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_7 = async_compile.triton('triton_poi_fused_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=[131072, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 131072
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ks/ckso6iiq5yfqfxmx7ilr6ufrmz6mlkiy75pexzhyf3ierq4pu3zl.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_8 = async_compile.triton('triton_poi_fused_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=[262144, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 262144
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (512*x2) + (4608*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cq/ccq66rrhrzjmgxnrmkqjfjou7btyc5dncveqmqkrdoivqkmduchd.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_9 = async_compile.triton('triton_poi_fused_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=[524288, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 524288
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (512*x2) + (4608*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/y7/cy74ayecev2pcofz3fyu6lc473nqeaato7assx62kzcpdkdyzi7o.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_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=[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_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 = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/m4/cm4tmz2dox7kspx56vkk5itntlk6q5wk6earntgeqc2tdnnw5uny.py
# Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_5 => 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_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': 9, '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 = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 2112) % 33
    x1 = (xindex // 64) % 33
    x0 = xindex % 64
    x3 = (xindex // 69696)
    x6 = xindex
    tmp0 = (-1) + (2*x2)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x1)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-4160) + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp10 & xmask, other=float("-inf"))
    tmp12 = 2*x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4096) + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + (2*x1)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-4032) + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2*x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-64) + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (64 + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + (2*x2)
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4032 + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4096 + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4160 + x0 + (128*x1) + (8192*x2) + (262144*x3)), tmp49 & xmask, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + (x6), tmp51, xmask)
    tl.store(out_ptr1 + (x6), tmp76, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qr/cqrf3s4gofvpfcdbph2hb2qpjaxdeqvv6ooxlnkbujmk2g4xkom6.py
# Topologically Sorted Source Nodes: [input_6, input_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_6 => convolution_2
#   input_7 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_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=[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_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 = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/g3/cg3tk442uxtpywouqjs6evitgqh4gwchniik7fwj7zdnegybg3kk.py
# Topologically Sorted Source Nodes: [input_10], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_10 => 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_13 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_13(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 2176) % 17
    x1 = (xindex // 128) % 17
    x0 = xindex % 128
    x3 = (xindex // 36992)
    x6 = xindex
    tmp0 = (-1) + (2*x2)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 33, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x1)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-4352) + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp10 & xmask, other=float("-inf"))
    tmp12 = 2*x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4224) + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + (2*x1)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-4096) + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2*x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-128) + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (128 + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + (2*x2)
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4224 + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4352 + x0 + (256*x1) + (8448*x2) + (139392*x3)), tmp49 & xmask, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + (x6), tmp51, xmask)
    tl.store(out_ptr1 + (x6), tmp76, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vl/cvln423fkkesrktxylki67mui6hfgsvccobjruueocneklu3vzqt.py
# Topologically Sorted Source Nodes: [input_11, input_12], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_11 => convolution_4
#   input_12 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
triton_poi_fused_convolution_relu_14 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_14', '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_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7l/c7l7or63ilrj6ossbec5hyaigyngtbfznz36ipverle2rskxa4ra.py
# Topologically Sorted Source Nodes: [input_17], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_17 => 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_15 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_15(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 2304) % 9
    x1 = (xindex // 256) % 9
    x0 = xindex % 256
    x3 = (xindex // 20736)
    x6 = xindex
    tmp0 = (-1) + (2*x2)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 17, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x1)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-4608) + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp10 & xmask, other=float("-inf"))
    tmp12 = 2*x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4352) + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + (2*x1)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-4096) + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2*x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-256) + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (256 + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + (2*x2)
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4352 + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4608 + x0 + (512*x1) + (8704*x2) + (73984*x3)), tmp49 & xmask, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + (x6), tmp51, xmask)
    tl.store(out_ptr1 + (x6), tmp76, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/kl/ckl2uk5k5wsi6rqmdfzciqm7fzx7rj3gu5hgnrhzuujepfd7w4ir.py
# Topologically Sorted Source Nodes: [input_18, input_19], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_18 => convolution_7
#   input_19 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_16 = async_compile.triton('triton_poi_fused_convolution_relu_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_16', '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_16(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/g7/cg7pb6hrndnb3e2hi73sdsofbt22mijsl3fxmv45esfo4rtt5nzx.py
# Topologically Sorted Source Nodes: [input_24], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_24 => 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_17 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_17(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 4608) % 9
    x1 = (xindex // 512) % 9
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 9, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5120) + x6), tmp10, other=float("-inf"))
    tmp12 = x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4608) + x6), tmp16, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-4096) + x6), tmp23, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-512) + x6), tmp30, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x6), tmp33, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (512 + x6), tmp36, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x6), tmp43, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4608 + x6), tmp46, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5120 + x6), tmp49, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + (x6), tmp51, None)
    tl.store(out_ptr1 + (x6), tmp76, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/lo/clox5kkbxp7t3rr66n7mewsmiwo2cfn7gwhgboarhn33datfmgc2.py
# Topologically Sorted Source Nodes: [input_32, input_33], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_32 => convolution_13
#   input_33 => relu_13
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_8, %primals_28, %primals_29, [1, 1], [6, 6], [6, 6], False, [0, 0], 1), kwargs = {})
#   %relu_13 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), 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 = 331776
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/z5/cz5imvlxnigwm7ldamm6jqognt5kvdyxf6ywzsepg4gghwi2t4ug.py
# Topologically Sorted Source Nodes: [input_38, input_45, input_52, input_59, add, add_1, add_2, add_3], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   input_38 => convolution_15
#   input_45 => convolution_18
#   input_52 => convolution_21
#   input_59 => convolution_24
# Graph fragment:
#   %convolution_15 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_14, %primals_32, %primals_33, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_16, %primals_38, %primals_39, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_18, %primals_44, %primals_45, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_24 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_20, %primals_50, %primals_51, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_15, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %convolution_18), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %convolution_21), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %convolution_24), kwargs = {})
triton_poi_fused_add_convolution_19 = async_compile.triton('triton_poi_fused_add_convolution_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=[512, 32], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_convolution_19(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 324
    xnumel = 21
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 81
    y1 = (yindex // 81)
    tmp0 = tl.load(in_ptr0 + (x2 + (21*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + (21*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + (x2 + (21*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr5 + (x2), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr6 + (x2 + (21*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr7 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tmp15 = tmp13 + tmp14
    tmp16 = tmp12 + tmp15
    tl.store(out_ptr0 + (y0 + (81*x2) + (1701*y1)), tmp16, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51 = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256, ), (1, ))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256, ), (1, ))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (512, ), (1, ))
    assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_19, (512, ), (1, ))
    assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_21, (512, ), (1, ))
    assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_23, (512, ), (1, ))
    assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (512, ), (1, ))
    assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_27, (512, ), (1, ))
    assert_size_stride(primals_28, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (1024, ), (1, ))
    assert_size_stride(primals_30, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_31, (1024, ), (1, ))
    assert_size_stride(primals_32, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_33, (21, ), (1, ))
    assert_size_stride(primals_34, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (1024, ), (1, ))
    assert_size_stride(primals_36, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_37, (1024, ), (1, ))
    assert_size_stride(primals_38, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_39, (21, ), (1, ))
    assert_size_stride(primals_40, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (1024, ), (1, ))
    assert_size_stride(primals_42, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_43, (1024, ), (1, ))
    assert_size_stride(primals_44, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_45, (21, ), (1, ))
    assert_size_stride(primals_46, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_47, (1024, ), (1, ))
    assert_size_stride(primals_48, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_49, (1024, ), (1, ))
    assert_size_stride(primals_50, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_51, (21, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 192, 9, grid=grid(192, 9), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 12, 4096, grid=grid(12, 4096), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_4, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_6, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_8, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_8
        buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_10, buf5, 32768, 9, grid=grid(32768, 9), stream=stream0)
        del primals_10
        buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_12, buf6, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_12
        buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_14, buf7, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_14
        buf8 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_7.run(primals_16, buf8, 131072, 9, grid=grid(131072, 9), stream=stream0)
        del primals_16
        buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_18, buf9, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_18
        buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_20, buf10, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_20
        buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_22, buf11, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_22
        buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_24, buf12, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_24
        buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_26, buf13, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_26
        buf14 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(primals_28, buf14, 524288, 9, grid=grid(524288, 9), stream=stream0)
        del primals_28
        buf15 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(primals_34, buf15, 524288, 9, grid=grid(524288, 9), stream=stream0)
        del primals_34
        buf16 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(primals_40, buf16, 524288, 9, grid=grid(524288, 9), stream=stream0)
        del primals_40
        buf17 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(primals_46, buf17, 524288, 9, grid=grid(524288, 9), stream=stream0)
        del primals_46
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf19, primals_2, 1048576, grid=grid(1048576), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [input_3, input_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf21, primals_5, 1048576, grid=grid(1048576), stream=stream0)
        del primals_5
        buf22 = empty_strided_cuda((4, 64, 33, 33), (69696, 1, 2112, 64), torch.float32)
        buf23 = empty_strided_cuda((4, 64, 33, 33), (69696, 1, 2112, 64), torch.int8)
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_11.run(buf21, buf22, buf23, 278784, grid=grid(278784), stream=stream0)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf22, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 128, 33, 33), (139392, 1, 4224, 128))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [input_6, input_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf25, primals_7, 557568, grid=grid(557568), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 33, 33), (139392, 1, 4224, 128))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [input_8, input_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf27, primals_9, 557568, grid=grid(557568), stream=stream0)
        del primals_9
        buf28 = empty_strided_cuda((4, 128, 17, 17), (36992, 1, 2176, 128), torch.float32)
        buf29 = empty_strided_cuda((4, 128, 17, 17), (36992, 1, 2176, 128), torch.int8)
        # Topologically Sorted Source Nodes: [input_10], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_13.run(buf27, buf28, buf29, 147968, grid=grid(147968), stream=stream0)
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf28, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [input_11, input_12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_14.run(buf31, primals_11, 295936, grid=grid(295936), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [input_13], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [input_13, input_14], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_14.run(buf33, primals_13, 295936, grid=grid(295936), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [input_15], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [input_15, input_16], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_14.run(buf35, primals_15, 295936, grid=grid(295936), stream=stream0)
        del primals_15
        buf36 = empty_strided_cuda((4, 256, 9, 9), (20736, 1, 2304, 256), torch.float32)
        buf37 = empty_strided_cuda((4, 256, 9, 9), (20736, 1, 2304, 256), torch.int8)
        # Topologically Sorted Source Nodes: [input_17], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_15.run(buf35, buf36, buf37, 82944, grid=grid(82944), stream=stream0)
        # Topologically Sorted Source Nodes: [input_18], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf36, buf8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf39 = buf38; del buf38  # reuse
        # Topologically Sorted Source Nodes: [input_18, input_19], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf39, primals_17, 165888, grid=grid(165888), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [input_20], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf39, buf9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf41 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [input_20, input_21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf41, primals_19, 165888, grid=grid(165888), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [input_22], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, buf10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf43 = buf42; del buf42  # reuse
        # Topologically Sorted Source Nodes: [input_22, input_23], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf43, primals_21, 165888, grid=grid(165888), stream=stream0)
        del primals_21
        buf44 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512), torch.float32)
        buf45 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512), torch.int8)
        # Topologically Sorted Source Nodes: [input_24], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_17.run(buf43, buf44, buf45, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [input_25], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf44, buf11, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf47 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [input_25, input_26], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf47, primals_23, 165888, grid=grid(165888), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [input_27], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, buf12, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf49 = buf48; del buf48  # reuse
        # Topologically Sorted Source Nodes: [input_27, input_28], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf49, primals_25, 165888, grid=grid(165888), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [input_29], Original ATen: [aten.convolution]
        buf50 = extern_kernels.convolution(buf49, buf13, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf51 = buf50; del buf50  # reuse
        # Topologically Sorted Source Nodes: [input_29, input_30], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_16.run(buf51, primals_27, 165888, grid=grid(165888), stream=stream0)
        del primals_27
        buf52 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512), torch.float32)
        buf53 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512), torch.int8)
        # Topologically Sorted Source Nodes: [input_31], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_17.run(buf51, buf52, buf53, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [input_32], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf52, buf14, stride=(1, 1), padding=(6, 6), dilation=(6, 6), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf55 = buf54; del buf54  # reuse
        # Topologically Sorted Source Nodes: [input_32, input_33], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf55, primals_29, 331776, grid=grid(331776), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [input_35], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_30, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf57 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [input_35, input_36], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf57, primals_31, 331776, grid=grid(331776), stream=stream0)
        del primals_31
        # Topologically Sorted Source Nodes: [input_38], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, primals_32, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 21, 9, 9), (1701, 1, 189, 21))
        # Topologically Sorted Source Nodes: [input_39], Original ATen: [aten.convolution]
        buf59 = extern_kernels.convolution(buf52, buf15, stride=(1, 1), padding=(12, 12), dilation=(12, 12), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf60 = buf59; del buf59  # reuse
        # Topologically Sorted Source Nodes: [input_39, input_40], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf60, primals_35, 331776, grid=grid(331776), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [input_42], Original ATen: [aten.convolution]
        buf61 = extern_kernels.convolution(buf60, primals_36, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf61, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf62 = buf61; del buf61  # reuse
        # Topologically Sorted Source Nodes: [input_42, input_43], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf62, primals_37, 331776, grid=grid(331776), stream=stream0)
        del primals_37
        # Topologically Sorted Source Nodes: [input_45], Original ATen: [aten.convolution]
        buf63 = extern_kernels.convolution(buf62, primals_38, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 21, 9, 9), (1701, 1, 189, 21))
        # Topologically Sorted Source Nodes: [input_46], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf52, buf16, stride=(1, 1), padding=(18, 18), dilation=(18, 18), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf65 = buf64; del buf64  # reuse
        # Topologically Sorted Source Nodes: [input_46, input_47], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf65, primals_41, 331776, grid=grid(331776), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [input_49], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf67 = buf66; del buf66  # reuse
        # Topologically Sorted Source Nodes: [input_49, input_50], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf67, primals_43, 331776, grid=grid(331776), stream=stream0)
        del primals_43
        # Topologically Sorted Source Nodes: [input_52], Original ATen: [aten.convolution]
        buf68 = extern_kernels.convolution(buf67, primals_44, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 21, 9, 9), (1701, 1, 189, 21))
        # Topologically Sorted Source Nodes: [input_53], Original ATen: [aten.convolution]
        buf69 = extern_kernels.convolution(buf52, buf17, stride=(1, 1), padding=(24, 24), dilation=(24, 24), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf70 = buf69; del buf69  # reuse
        # Topologically Sorted Source Nodes: [input_53, input_54], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf70, primals_47, 331776, grid=grid(331776), stream=stream0)
        del primals_47
        # Topologically Sorted Source Nodes: [input_56], Original ATen: [aten.convolution]
        buf71 = extern_kernels.convolution(buf70, primals_48, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf71, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf72 = buf71; del buf71  # reuse
        # Topologically Sorted Source Nodes: [input_56, input_57], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_18.run(buf72, primals_49, 331776, grid=grid(331776), stream=stream0)
        del primals_49
        # Topologically Sorted Source Nodes: [input_59], Original ATen: [aten.convolution]
        buf73 = extern_kernels.convolution(buf72, primals_50, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 21, 9, 9), (1701, 1, 189, 21))
        buf74 = empty_strided_cuda((4, 21, 9, 9), (1701, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_38, input_45, input_52, input_59, add, add_1, add_2, add_3], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_19.run(buf58, primals_33, buf63, primals_39, buf68, primals_45, buf73, primals_51, buf74, 324, 21, grid=grid(324, 21), stream=stream0)
        del buf58
        del buf63
        del buf68
        del buf73
        del primals_33
        del primals_39
        del primals_45
        del primals_51
    return (buf74, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12, buf13, buf14, primals_30, primals_32, buf15, primals_36, primals_38, buf16, primals_42, primals_44, buf17, primals_48, primals_50, buf19, buf21, buf22, buf23, buf25, buf27, buf28, buf29, buf31, buf33, buf35, buf36, buf37, buf39, buf41, buf43, buf44, buf45, buf47, buf49, buf51, buf52, buf53, buf55, buf57, buf60, buf62, buf65, buf67, buf70, buf72, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((21, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((21, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((21, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((21, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((21, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((21, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((21, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((21, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51])
    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


class FCReLUDrop(nn.Sequential):

    def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
        layer_idx, branch_idx):
        super(FCReLUDrop, self).__init__()
        self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
            out_ch, kernel_size, stride=1, padding=padding, dilation=dilation))
        self.add_module(f'relu{layer_idx}_{branch_idx}', nn.ReLU(inplace=True))
        self.add_module(f'drop{layer_idx}_{branch_idx}', nn.Dropout(p=0.5))


class VGGASPPBranch(nn.Sequential):

    def __init__(self, in_ch, num_classes, rate, start_layer_idx,
        branch_idx, net_id):
        super(VGGASPPBranch, self).__init__()
        self.add_module(f'aspp_layer{start_layer_idx}_{branch_idx}',
            FCReLUDrop(in_ch, out_ch=1024, kernel_size=3, dilation=rate,
            padding=rate, layer_idx=start_layer_idx, branch_idx=branch_idx))
        self.add_module(f'aspp_layer{start_layer_idx + 1}_{branch_idx}',
            FCReLUDrop(in_ch=1024, out_ch=1024, kernel_size=1, dilation=1,
            padding=0, layer_idx=start_layer_idx + 1, branch_idx=branch_idx))
        self.add_module(f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}',
            nn.Conv2d(in_channels=1024, out_channels=num_classes,
            kernel_size=1))
        fc_logit = eval('self.' +
            f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}')
        nn.init.normal_(fc_logit.weight, mean=0.0, std=0.01)
        nn.init.constant_(fc_logit.bias, 0.0)


class VGGASPP(nn.Module):

    def __init__(self, in_ch, num_classes, rates, start_layer_idx, net_id=
        'pascal'):
        super(VGGASPP, self).__init__()
        for rate, branch_idx in zip(rates, range(1, len(rates) + 1)):
            self.add_module(f'aspp_branch{branch_idx}', VGGASPPBranch(in_ch,
                num_classes, rate, start_layer_idx, branch_idx, net_id))

    def forward(self, x):
        return sum([branch(x) for branch in self.children()])


class ConvReLU(nn.Sequential):

    def __init__(self, in_ch, out_ch, dilation, layer_idx, seq_idx):
        super(ConvReLU, self).__init__()
        self.add_module(f'conv{layer_idx}_{seq_idx}', nn.Conv2d(in_channels
            =in_ch, out_channels=out_ch, kernel_size=3, padding=dilation,
            dilation=dilation))
        self.add_module(f'relu{layer_idx}_{seq_idx}', nn.ReLU(inplace=True))


class VGGLayer(nn.Sequential):

    def __init__(self, in_ch, out_ch, conv_num, dilation, pool_size,
        pool_stride, layer_idx):
        super(VGGLayer, self).__init__()
        for seq_idx in range(1, conv_num + 1):
            self.add_module(f'conv_relu_{seq_idx}', ConvReLU(in_ch=in_ch if
                seq_idx == 1 else out_ch, out_ch=out_ch, dilation=dilation,
                layer_idx=layer_idx, seq_idx=seq_idx))
        self.add_module(f'pool{layer_idx}', nn.MaxPool2d(kernel_size=
            pool_size, stride=pool_stride, padding=pool_size % 2, ceil_mode
            =True))


class VGGFeature(nn.Sequential):

    def __init__(self, in_ch, out_chs, conv_nums, dilations, pool_strides,
        pool_size):
        super(VGGFeature, self).__init__()
        for i, layer_idx in enumerate(range(1, len(out_chs) + 1)):
            self.add_module(f'layer{layer_idx}', VGGLayer(in_ch=in_ch if 
                layer_idx == 1 else out_chs[i - 1], out_ch=out_chs[i],
                conv_num=conv_nums[i], dilation=dilations[i], pool_size=
                pool_size, pool_stride=pool_strides[i], layer_idx=layer_idx))


class VOC_VGG16_DeepLabV2(nn.Module):

    def __init__(self):
        super(VOC_VGG16_DeepLabV2, self).__init__()
        self.VGGFeature = VGGFeature(in_ch=3, out_chs=[64, 128, 256, 512, 
            512], conv_nums=[2, 2, 3, 3, 3], dilations=[1, 1, 1, 1, 2],
            pool_strides=[2, 2, 2, 1, 1], pool_size=3)
        self.VGGASPP = VGGASPP(in_ch=512, num_classes=21, rates=[6, 12, 18,
            24], start_layer_idx=6, net_id='pascal')

    def forward(self, x):
        x = self.VGGFeature(x)
        return self.VGGASPP(x)


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 192
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@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)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_11(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 2112 % 33
    x1 = xindex // 64 % 33
    x0 = xindex % 64
    x3 = xindex // 69696
    x6 = xindex
    tmp0 = -1 + 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-4160 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp10 & xmask, other=float('-inf'))
    tmp12 = 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4096 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp16 & xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-4032 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp23 & xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 * x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-64 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp30 & xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2 + 262144 * x3), 
        tmp33 & xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2 + 262144 * x3
        ), tmp36 & xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + 2 * x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4032 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp43 & xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp46 & xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2 + 262144 *
        x3), tmp49 & xmask, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x6, tmp51, xmask)
    tl.store(out_ptr1 + x6, tmp76, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_13(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 2176 % 17
    x1 = xindex // 128 % 17
    x0 = xindex % 128
    x3 = xindex // 36992
    x6 = xindex
    tmp0 = -1 + 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 33, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-4352 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp10 & xmask, other=float('-inf'))
    tmp12 = 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4224 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp16 & xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-4096 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp23 & xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 * x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-128 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp30 & xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8448 * x2 + 139392 * x3), 
        tmp33 & xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp36 & xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + 2 * x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp43 & xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp46 & xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4352 + x0 + 256 * x1 + 8448 * x2 + 139392 *
        x3), tmp49 & xmask, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x6, tmp51, xmask)
    tl.store(out_ptr1 + x6, tmp76, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_14(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_15(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 2304 % 9
    x1 = xindex // 256 % 9
    x0 = xindex % 256
    x3 = xindex // 20736
    x6 = xindex
    tmp0 = -1 + 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 17, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-4608 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp10 & xmask, other=float('-inf'))
    tmp12 = 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4352 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp16 & xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-4096 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp23 & xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 * x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-256 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp30 & xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + 512 * x1 + 8704 * x2 + 73984 * x3), 
        tmp33 & xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 8704 * x2 + 73984 * x3
        ), tmp36 & xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + 2 * x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp43 & xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4352 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp46 & xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (4608 + x0 + 512 * x1 + 8704 * x2 + 73984 *
        x3), tmp49 & xmask, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x6, tmp51, xmask)
    tl.store(out_ptr1 + x6, tmp76, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_16(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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_17(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)
    x2 = xindex // 4608 % 9
    x1 = xindex // 512 % 9
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 9, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5120 + x6), tmp10, other=float('-inf'))
    tmp12 = x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4608 + x6), tmp16, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-4096 + x6), tmp23, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-512 + x6), tmp30, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x6, tmp33, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (512 + x6), tmp36, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (4096 + x6), tmp43, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4608 + x6), tmp46, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5120 + x6), tmp49, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x6, tmp51, None)
    tl.store(out_ptr1 + x6, tmp76, 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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_add_convolution_19(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 324
    xnumel = 21
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 81
    y1 = yindex // 81
    tmp0 = tl.load(in_ptr0 + (x2 + 21 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + 21 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp6 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + (x2 + 21 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp10 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr6 + (x2 + 21 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr7 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tmp15 = tmp13 + tmp14
    tmp16 = tmp12 + tmp15
    tl.store(out_ptr0 + (y0 + 81 * x2 + 1701 * y1), tmp16, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_19, (512,), (1,))
    assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_21, (512,), (1,))
    assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_23, (512,), (1,))
    assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_25, (512,), (1,))
    assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_27, (512,), (1,))
    assert_size_stride(primals_28, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_29, (1024,), (1,))
    assert_size_stride(primals_30, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_31, (1024,), (1,))
    assert_size_stride(primals_32, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_33, (21,), (1,))
    assert_size_stride(primals_34, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_35, (1024,), (1,))
    assert_size_stride(primals_36, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_37, (1024,), (1,))
    assert_size_stride(primals_38, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_39, (21,), (1,))
    assert_size_stride(primals_40, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_41, (1024,), (1,))
    assert_size_stride(primals_42, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_43, (1024,), (1,))
    assert_size_stride(primals_44, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_45, (21,), (1,))
    assert_size_stride(primals_46, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_47, (1024,), (1,))
    assert_size_stride(primals_48, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_49, (1024,), (1,))
    assert_size_stride(primals_50, (21, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_51, (21,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(192, 9)](primals_1, buf0, 192, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_2[grid(4096, 9)](primals_4, buf2, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_3[grid(8192, 9)](primals_6, buf3, 8192, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_4[grid(16384, 9)](primals_8, buf4, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_5[grid(32768, 9)](primals_10, buf5, 32768, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_6[grid(65536, 9)](primals_12, buf6, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_12
        buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_6[grid(65536, 9)](primals_14, buf7, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf8 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_7[grid(131072, 9)](primals_16, buf8, 131072, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_16
        buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_8[grid(262144, 9)](primals_18, buf9, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_18
        buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_8[grid(262144, 9)](primals_20, buf10, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_20
        buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_8[grid(262144, 9)](primals_22, buf11, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_22
        buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_8[grid(262144, 9)](primals_24, buf12, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_24
        buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_8[grid(262144, 9)](primals_26, buf13, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_26
        buf14 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_9[grid(524288, 9)](primals_28, buf14, 524288, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_28
        buf15 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_9[grid(524288, 9)](primals_34, buf15, 524288, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_34
        buf16 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_9[grid(524288, 9)](primals_40, buf16, 524288, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_40
        buf17 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_9[grid(524288, 9)](primals_46, buf17, 524288, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_46
        buf18 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_10[grid(1048576)](buf19,
            primals_2, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf20 = extern_kernels.convolution(buf19, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_10[grid(1048576)](buf21,
            primals_5, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf22 = empty_strided_cuda((4, 64, 33, 33), (69696, 1, 2112, 64),
            torch.float32)
        buf23 = empty_strided_cuda((4, 64, 33, 33), (69696, 1, 2112, 64),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_11[grid(278784)](buf21,
            buf22, buf23, 278784, XBLOCK=512, num_warps=8, num_stages=1)
        buf24 = extern_kernels.convolution(buf22, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 128, 33, 33), (139392, 1, 4224, 128))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_12[grid(557568)](buf25, primals_7,
            557568, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf26 = extern_kernels.convolution(buf25, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 33, 33), (139392, 1, 4224, 128))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_12[grid(557568)](buf27, primals_9,
            557568, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf28 = empty_strided_cuda((4, 128, 17, 17), (36992, 1, 2176, 128),
            torch.float32)
        buf29 = empty_strided_cuda((4, 128, 17, 17), (36992, 1, 2176, 128),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_13[grid(147968)](buf27,
            buf28, buf29, 147968, XBLOCK=512, num_warps=8, num_stages=1)
        buf30 = extern_kernels.convolution(buf28, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_relu_14[grid(295936)](buf31,
            primals_11, 295936, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf32 = extern_kernels.convolution(buf31, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_relu_14[grid(295936)](buf33,
            primals_13, 295936, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_13
        buf34 = extern_kernels.convolution(buf33, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 256, 17, 17), (73984, 1, 4352, 256))
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_relu_14[grid(295936)](buf35,
            primals_15, 295936, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_15
        buf36 = empty_strided_cuda((4, 256, 9, 9), (20736, 1, 2304, 256),
            torch.float32)
        buf37 = empty_strided_cuda((4, 256, 9, 9), (20736, 1, 2304, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_15[grid(82944)](buf35,
            buf36, buf37, 82944, XBLOCK=512, num_warps=8, num_stages=1)
        buf38 = extern_kernels.convolution(buf36, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf39 = buf38
        del buf38
        triton_poi_fused_convolution_relu_16[grid(165888)](buf39,
            primals_17, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf40 = extern_kernels.convolution(buf39, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf41 = buf40
        del buf40
        triton_poi_fused_convolution_relu_16[grid(165888)](buf41,
            primals_19, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_19
        buf42 = extern_kernels.convolution(buf41, buf10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf43 = buf42
        del buf42
        triton_poi_fused_convolution_relu_16[grid(165888)](buf43,
            primals_21, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_21
        buf44 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
            torch.float32)
        buf45 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_17[grid(165888)](buf43,
            buf44, buf45, 165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf46 = extern_kernels.convolution(buf44, buf11, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf47 = buf46
        del buf46
        triton_poi_fused_convolution_relu_16[grid(165888)](buf47,
            primals_23, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_23
        buf48 = extern_kernels.convolution(buf47, buf12, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf49 = buf48
        del buf48
        triton_poi_fused_convolution_relu_16[grid(165888)](buf49,
            primals_25, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_25
        buf50 = extern_kernels.convolution(buf49, buf13, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf50, (4, 512, 9, 9), (41472, 1, 4608, 512))
        buf51 = buf50
        del buf50
        triton_poi_fused_convolution_relu_16[grid(165888)](buf51,
            primals_27, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_27
        buf52 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
            torch.float32)
        buf53 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_17[grid(165888)](buf51,
            buf52, buf53, 165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf54 = extern_kernels.convolution(buf52, buf14, stride=(1, 1),
            padding=(6, 6), dilation=(6, 6), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf55 = buf54
        del buf54
        triton_poi_fused_convolution_relu_18[grid(331776)](buf55,
            primals_29, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_29
        buf56 = extern_kernels.convolution(buf55, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf57 = buf56
        del buf56
        triton_poi_fused_convolution_relu_18[grid(331776)](buf57,
            primals_31, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_31
        buf58 = extern_kernels.convolution(buf57, primals_32, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 21, 9, 9), (1701, 1, 189, 21))
        buf59 = extern_kernels.convolution(buf52, buf15, stride=(1, 1),
            padding=(12, 12), dilation=(12, 12), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf60 = buf59
        del buf59
        triton_poi_fused_convolution_relu_18[grid(331776)](buf60,
            primals_35, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_35
        buf61 = extern_kernels.convolution(buf60, primals_36, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf61, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf62 = buf61
        del buf61
        triton_poi_fused_convolution_relu_18[grid(331776)](buf62,
            primals_37, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_37
        buf63 = extern_kernels.convolution(buf62, primals_38, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 21, 9, 9), (1701, 1, 189, 21))
        buf64 = extern_kernels.convolution(buf52, buf16, stride=(1, 1),
            padding=(18, 18), dilation=(18, 18), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf65 = buf64
        del buf64
        triton_poi_fused_convolution_relu_18[grid(331776)](buf65,
            primals_41, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_41
        buf66 = extern_kernels.convolution(buf65, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf67 = buf66
        del buf66
        triton_poi_fused_convolution_relu_18[grid(331776)](buf67,
            primals_43, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_43
        buf68 = extern_kernels.convolution(buf67, primals_44, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 21, 9, 9), (1701, 1, 189, 21))
        buf69 = extern_kernels.convolution(buf52, buf17, stride=(1, 1),
            padding=(24, 24), dilation=(24, 24), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf69, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf70 = buf69
        del buf69
        triton_poi_fused_convolution_relu_18[grid(331776)](buf70,
            primals_47, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_47
        buf71 = extern_kernels.convolution(buf70, primals_48, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf71, (4, 1024, 9, 9), (82944, 1, 9216, 1024))
        buf72 = buf71
        del buf71
        triton_poi_fused_convolution_relu_18[grid(331776)](buf72,
            primals_49, 331776, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_49
        buf73 = extern_kernels.convolution(buf72, primals_50, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 21, 9, 9), (1701, 1, 189, 21))
        buf74 = empty_strided_cuda((4, 21, 9, 9), (1701, 81, 9, 1), torch.
            float32)
        triton_poi_fused_add_convolution_19[grid(324, 21)](buf58,
            primals_33, buf63, primals_39, buf68, primals_45, buf73,
            primals_51, buf74, 324, 21, XBLOCK=32, YBLOCK=8, num_warps=4,
            num_stages=1)
        del buf58
        del buf63
        del buf68
        del buf73
        del primals_33
        del primals_39
        del primals_45
        del primals_51
    return (buf74, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
        buf9, buf10, buf11, buf12, buf13, buf14, primals_30, primals_32,
        buf15, primals_36, primals_38, buf16, primals_42, primals_44, buf17,
        primals_48, primals_50, buf19, buf21, buf22, buf23, buf25, buf27,
        buf28, buf29, buf31, buf33, buf35, buf36, buf37, buf39, buf41,
        buf43, buf44, buf45, buf47, buf49, buf51, buf52, buf53, buf55,
        buf57, buf60, buf62, buf65, buf67, buf70, buf72)


class FCReLUDrop(nn.Sequential):

    def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
        layer_idx, branch_idx):
        super(FCReLUDrop, self).__init__()
        self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
            out_ch, kernel_size, stride=1, padding=padding, dilation=dilation))
        self.add_module(f'relu{layer_idx}_{branch_idx}', nn.ReLU(inplace=True))
        self.add_module(f'drop{layer_idx}_{branch_idx}', nn.Dropout(p=0.5))


class VGGASPPBranch(nn.Sequential):

    def __init__(self, in_ch, num_classes, rate, start_layer_idx,
        branch_idx, net_id):
        super(VGGASPPBranch, self).__init__()
        self.add_module(f'aspp_layer{start_layer_idx}_{branch_idx}',
            FCReLUDrop(in_ch, out_ch=1024, kernel_size=3, dilation=rate,
            padding=rate, layer_idx=start_layer_idx, branch_idx=branch_idx))
        self.add_module(f'aspp_layer{start_layer_idx + 1}_{branch_idx}',
            FCReLUDrop(in_ch=1024, out_ch=1024, kernel_size=1, dilation=1,
            padding=0, layer_idx=start_layer_idx + 1, branch_idx=branch_idx))
        self.add_module(f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}',
            nn.Conv2d(in_channels=1024, out_channels=num_classes,
            kernel_size=1))
        fc_logit = eval('self.' +
            f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}')
        nn.init.normal_(fc_logit.weight, mean=0.0, std=0.01)
        nn.init.constant_(fc_logit.bias, 0.0)


class VGGASPP(nn.Module):

    def __init__(self, in_ch, num_classes, rates, start_layer_idx, net_id=
        'pascal'):
        super(VGGASPP, self).__init__()
        for rate, branch_idx in zip(rates, range(1, len(rates) + 1)):
            self.add_module(f'aspp_branch{branch_idx}', VGGASPPBranch(in_ch,
                num_classes, rate, start_layer_idx, branch_idx, net_id))

    def forward(self, x):
        return sum([branch(x) for branch in self.children()])


class ConvReLU(nn.Sequential):

    def __init__(self, in_ch, out_ch, dilation, layer_idx, seq_idx):
        super(ConvReLU, self).__init__()
        self.add_module(f'conv{layer_idx}_{seq_idx}', nn.Conv2d(in_channels
            =in_ch, out_channels=out_ch, kernel_size=3, padding=dilation,
            dilation=dilation))
        self.add_module(f'relu{layer_idx}_{seq_idx}', nn.ReLU(inplace=True))


class VGGLayer(nn.Sequential):

    def __init__(self, in_ch, out_ch, conv_num, dilation, pool_size,
        pool_stride, layer_idx):
        super(VGGLayer, self).__init__()
        for seq_idx in range(1, conv_num + 1):
            self.add_module(f'conv_relu_{seq_idx}', ConvReLU(in_ch=in_ch if
                seq_idx == 1 else out_ch, out_ch=out_ch, dilation=dilation,
                layer_idx=layer_idx, seq_idx=seq_idx))
        self.add_module(f'pool{layer_idx}', nn.MaxPool2d(kernel_size=
            pool_size, stride=pool_stride, padding=pool_size % 2, ceil_mode
            =True))


class VGGFeature(nn.Sequential):

    def __init__(self, in_ch, out_chs, conv_nums, dilations, pool_strides,
        pool_size):
        super(VGGFeature, self).__init__()
        for i, layer_idx in enumerate(range(1, len(out_chs) + 1)):
            self.add_module(f'layer{layer_idx}', VGGLayer(in_ch=in_ch if 
                layer_idx == 1 else out_chs[i - 1], out_ch=out_chs[i],
                conv_num=conv_nums[i], dilation=dilations[i], pool_size=
                pool_size, pool_stride=pool_strides[i], layer_idx=layer_idx))


class VOC_VGG16_DeepLabV2New(nn.Module):

    def __init__(self):
        super(VOC_VGG16_DeepLabV2New, self).__init__()
        self.VGGFeature = VGGFeature(in_ch=3, out_chs=[64, 128, 256, 512, 
            512], conv_nums=[2, 2, 3, 3, 3], dilations=[1, 1, 1, 1, 2],
            pool_strides=[2, 2, 2, 1, 1], pool_size=3)
        self.VGGASPP = VGGASPP(in_ch=512, num_classes=21, rates=[6, 12, 18,
            24], start_layer_idx=6, net_id='pascal')

    def forward(self, input_0):
        primals_1 = self.VGGFeature.layer1.conv_relu_1.conv1_1.weight
        primals_2 = self.VGGFeature.layer1.conv_relu_1.conv1_1.bias
        primals_4 = self.VGGFeature.layer1.conv_relu_2.conv1_2.weight
        primals_5 = self.VGGFeature.layer1.conv_relu_2.conv1_2.bias
        primals_6 = self.VGGFeature.layer2.conv_relu_1.conv2_1.weight
        primals_7 = self.VGGFeature.layer2.conv_relu_1.conv2_1.bias
        primals_8 = self.VGGFeature.layer2.conv_relu_2.conv2_2.weight
        primals_9 = self.VGGFeature.layer2.conv_relu_2.conv2_2.bias
        primals_10 = self.VGGFeature.layer3.conv_relu_1.conv3_1.weight
        primals_11 = self.VGGFeature.layer3.conv_relu_1.conv3_1.bias
        primals_12 = self.VGGFeature.layer3.conv_relu_2.conv3_2.weight
        primals_13 = self.VGGFeature.layer3.conv_relu_2.conv3_2.bias
        primals_14 = self.VGGFeature.layer3.conv_relu_3.conv3_3.weight
        primals_15 = self.VGGFeature.layer3.conv_relu_3.conv3_3.bias
        primals_16 = self.VGGFeature.layer4.conv_relu_1.conv4_1.weight
        primals_17 = self.VGGFeature.layer4.conv_relu_1.conv4_1.bias
        primals_18 = self.VGGFeature.layer4.conv_relu_2.conv4_2.weight
        primals_19 = self.VGGFeature.layer4.conv_relu_2.conv4_2.bias
        primals_20 = self.VGGFeature.layer4.conv_relu_3.conv4_3.weight
        primals_21 = self.VGGFeature.layer4.conv_relu_3.conv4_3.bias
        primals_22 = self.VGGFeature.layer5.conv_relu_1.conv5_1.weight
        primals_23 = self.VGGFeature.layer5.conv_relu_1.conv5_1.bias
        primals_24 = self.VGGFeature.layer5.conv_relu_2.conv5_2.weight
        primals_25 = self.VGGFeature.layer5.conv_relu_2.conv5_2.bias
        primals_26 = self.VGGFeature.layer5.conv_relu_3.conv5_3.weight
        primals_27 = self.VGGFeature.layer5.conv_relu_3.conv5_3.bias
        primals_28 = self.VGGASPP.aspp_branch1.aspp_layer6_1.fc6_1.weight
        primals_29 = self.VGGASPP.aspp_branch1.aspp_layer6_1.fc6_1.bias
        primals_30 = self.VGGASPP.aspp_branch1.aspp_layer7_1.fc7_1.weight
        primals_31 = self.VGGASPP.aspp_branch1.aspp_layer7_1.fc7_1.bias
        primals_32 = self.VGGASPP.aspp_branch1.fc8_pascal_1.weight
        primals_33 = self.VGGASPP.aspp_branch1.fc8_pascal_1.bias
        primals_34 = self.VGGASPP.aspp_branch2.aspp_layer6_2.fc6_2.weight
        primals_35 = self.VGGASPP.aspp_branch2.aspp_layer6_2.fc6_2.bias
        primals_36 = self.VGGASPP.aspp_branch2.aspp_layer7_2.fc7_2.weight
        primals_37 = self.VGGASPP.aspp_branch2.aspp_layer7_2.fc7_2.bias
        primals_38 = self.VGGASPP.aspp_branch2.fc8_pascal_2.weight
        primals_39 = self.VGGASPP.aspp_branch2.fc8_pascal_2.bias
        primals_40 = self.VGGASPP.aspp_branch3.aspp_layer6_3.fc6_3.weight
        primals_41 = self.VGGASPP.aspp_branch3.aspp_layer6_3.fc6_3.bias
        primals_42 = self.VGGASPP.aspp_branch3.aspp_layer7_3.fc7_3.weight
        primals_43 = self.VGGASPP.aspp_branch3.aspp_layer7_3.fc7_3.bias
        primals_44 = self.VGGASPP.aspp_branch3.fc8_pascal_3.weight
        primals_45 = self.VGGASPP.aspp_branch3.fc8_pascal_3.bias
        primals_46 = self.VGGASPP.aspp_branch4.aspp_layer6_4.fc6_4.weight
        primals_47 = self.VGGASPP.aspp_branch4.aspp_layer6_4.fc6_4.bias
        primals_48 = self.VGGASPP.aspp_branch4.aspp_layer7_4.fc7_4.weight
        primals_49 = self.VGGASPP.aspp_branch4.aspp_layer7_4.fc7_4.bias
        primals_50 = self.VGGASPP.aspp_branch4.fc8_pascal_4.weight
        primals_51 = self.VGGASPP.aspp_branch4.fc8_pascal_4.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51])
        return output[0]
