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

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


# kernel path: runs/run_shard_4/inductor_cache/ze/czeyd3qjsq546c7ea763ybzbn4sb4zzidmbxe2coosrykwwb4pit.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=[512, 64], 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 = 288
    xnumel = 49
    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 + (49*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (147*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/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_4/inductor_cache/sf/csfphurxkfilliqpsa7cfr3pqkfaef7yr7uzm2nhhxuzpah3kv4x.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=[1024, 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 = 1024
    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 % 16
    y1 = (yindex // 16)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (16*x2) + (144*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/k6/ck6mpoqrm3een2gnzk3q7avn7if4q5njkh6yuf2lcdtfooev6ukp.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=[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_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 = 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 % 32
    y1 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (32*x2) + (288*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/e3/ce3476wuixbg7whdmceldres75gmr262efy4sgv4xcritwmi4xir.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 = 9216
    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 % 48
    y1 = (yindex // 48)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (48*x2) + (432*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wa/cwasc5xshefzagbizx6nhfjaifdz7vqj4evbpydruvtdugd4lhfp.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=[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_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 = 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 % 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_4/inductor_cache/4j/c4jshfefqkzdtvcidkjhrzjj55ta4bzr5nwbf2nuzdoc75mmiayw.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, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_6 = async_compile.triton('triton_poi_fused_convolution_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 322944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 96
    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_4/inductor_cache/hn/chn7w4dttiseahpxxhjjrfcqpaj5jhrmdzbhyzmmkrkwf57xro7q.py
# Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_3 => 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_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_7', '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_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 75264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 96
    x1 = (xindex // 96) % 14
    x2 = (xindex // 1344) % 14
    x3 = (xindex // 18816)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp1 = tl.load(in_ptr0 + (96 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp3 = tl.load(in_ptr0 + (192 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp5 = tl.load(in_ptr0 + (2784 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp7 = tl.load(in_ptr0 + (2880 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp9 = tl.load(in_ptr0 + (2976 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp11 = tl.load(in_ptr0 + (5568 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp13 = tl.load(in_ptr0 + (5664 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp15 = tl.load(in_ptr0 + (5760 + x0 + (192*x1) + (5568*x2) + (80736*x3)), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + (x4), tmp16, xmask)
    tl.store(out_ptr1 + (x4), tmp41, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/n2/cn2zhaw5q6d3p7elstsunhq6ybcjaqtshcx4id6mvj7r7pp6muc7.py
# Topologically Sorted Source Nodes: [conv2d_1, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   x => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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_4/inductor_cache/56/c56yi7b4gudcyxqcuy62nlzwkpzyvbfci2mdojw3gbunflpxfmwb.py
# Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_4 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_2, %relu_3], 1), kwargs = {})
triton_poi_fused_cat_9 = async_compile.triton('triton_poi_fused_cat_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100352
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((64*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 128, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-64) + x0), tmp12, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qe/cqeo4l5igb7ssqpg4qcf256ohiqzstzkbcwi5m3qi4a33t2cbk6c.py
# Topologically Sorted Source Nodes: [conv2d_7, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_7 => convolution_7
#   x_2 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_10 = async_compile.triton('triton_poi_fused_convolution_relu_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_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 = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_4/inductor_cache/fj/cfj6u5lcpgelhrmdirkhqtdee5oy6idtzgzhl5xjlut6mwmcq5ez.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_6 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_8, %relu_9], 1), kwargs = {})
triton_poi_fused_cat_11 = async_compile.triton('triton_poi_fused_cat_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 200704
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = (xindex // 256)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((128*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 256, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((128*x1) + ((-128) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-128) + x0), tmp12, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/l3/cl3lx4bd6l5at6p2m7izsqz2tw7jh6dw7dimwozxxwufrimtfiqz.py
# Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_7 => 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_12 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_12', '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_12(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 1792) % 7
    x1 = (xindex // 256) % 7
    x0 = xindex % 256
    x5 = (xindex // 1792)
    x6 = xindex
    tmp0 = 2*x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 14, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = 2*x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (x0 + (512*x1) + (7168*x5)), tmp10 & xmask, other=float("-inf"))
    tmp12 = 1 + (2*x1)
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (256 + x0 + (512*x1) + (7168*x5)), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 + (2*x1)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (512 + x0 + (512*x1) + (7168*x5)), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 1 + (2*x2)
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (3584 + x0 + (512*x1) + (7168*x5)), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (3840 + x0 + (512*x1) + (7168*x5)), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (4096 + x0 + (512*x1) + (7168*x5)), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 2 + (2*x2)
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (7168 + x0 + (512*x1) + (7168*x5)), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (7424 + x0 + (512*x1) + (7168*x5)), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (7680 + x0 + (512*x1) + (7168*x5)), 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_4/inductor_cache/my/cmyhxeiv3z2ual3ueuhemlx25ba554moz6ij24ubkagg3qxd2jmk.py
# Topologically Sorted Source Nodes: [conv2d_10, x_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_10
#   x_3 => relu_10
# Graph fragment:
#   %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
triton_poi_fused_convolution_relu_13 = async_compile.triton('triton_poi_fused_convolution_relu_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_4/inductor_cache/b2/cb2xdk2o6mgvhtsoy5gptyuqevcymafmzpecvobhsolx4covjirs.py
# Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_8 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_11, %relu_12], 1), kwargs = {})
triton_poi_fused_cat_14 = async_compile.triton('triton_poi_fused_cat_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_14(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x1 = (xindex // 256)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((128*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 256, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((128*x1) + ((-128) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-128) + x0), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pa/cpazhbfndas4amtcw2kihst5qrnlydkbieelfyb5h22pw4z5wwqp.py
# Topologically Sorted Source Nodes: [conv2d_13, x_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_13 => convolution_13
#   x_4 => relu_13
# Graph fragment:
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_28, %primals_29, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_13 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {})
triton_poi_fused_convolution_relu_15 = async_compile.triton('triton_poi_fused_convolution_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_15', '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_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 48
    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_4/inductor_cache/wb/cwbn5mqw2v2zzldjs4ac7oua67izn3hsaqm62k4yghm3yuppsilx.py
# Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_9 => cat_4
# Graph fragment:
#   %cat_4 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_14, %relu_15], 1), kwargs = {})
triton_poi_fused_cat_16 = async_compile.triton('triton_poi_fused_cat_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_16(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 75264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 384
    x1 = (xindex // 384)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 192, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((192*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 384, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((192*x1) + ((-192) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-192) + x0), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5c/c5cxr2sgesuh3ykfbyxbkhz6ci32ejiuiodkmjw2os5hw3xwjxoh.py
# Topologically Sorted Source Nodes: [conv2d_19, x_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_19 => convolution_19
#   x_6 => relu_19
# Graph fragment:
#   %convolution_19 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_5, %primals_40, %primals_41, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_19 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_19,), kwargs = {})
triton_poi_fused_convolution_relu_17 = async_compile.triton('triton_poi_fused_convolution_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_17', '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_17(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_4/inductor_cache/go/cgog67rtzwolkpyjsykd37vy6ax6lzk7vkr6q33ga6ewlzj7xpgx.py
# Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_11 => cat_6
# Graph fragment:
#   %cat_6 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_20, %relu_21], 1), kwargs = {})
triton_poi_fused_cat_18 = async_compile.triton('triton_poi_fused_cat_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_18(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100352
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = (xindex // 512)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((256*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 512, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((256*x1) + ((-256) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-256) + x0), tmp12, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pp/cppl4cftexd7sjwxqm6twssr34eku3qwgtvw3vck2g5ezk5nery6.py
# Topologically Sorted Source Nodes: [input_12], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_12 => 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_19 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_19', '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_19(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = (xindex // 512) % 3
    x2 = (xindex // 1536) % 3
    x3 = (xindex // 4608)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp1 = tl.load(in_ptr0 + (512 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp3 = tl.load(in_ptr0 + (1024 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp5 = tl.load(in_ptr0 + (3584 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp7 = tl.load(in_ptr0 + (4096 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp9 = tl.load(in_ptr0 + (4608 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp11 = tl.load(in_ptr0 + (7168 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp13 = tl.load(in_ptr0 + (7680 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp15 = tl.load(in_ptr0 + (8192 + x0 + (1024*x1) + (7168*x2) + (25088*x3)), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + (x4), tmp16, None)
    tl.store(out_ptr1 + (x4), tmp41, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/45/c45jgdzesyuhk6beyhfbmirm3tamhabwhi5pwvke7xes3jeijxrf.py
# Topologically Sorted Source Nodes: [conv2d_22, x_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_22 => convolution_22
#   x_7 => relu_22
# Graph fragment:
#   %convolution_22 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_46, %primals_47, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_22 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_22,), kwargs = {})
triton_poi_fused_convolution_relu_20 = async_compile.triton('triton_poi_fused_convolution_relu_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4096], 
    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_20', '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_20(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_4/inductor_cache/5r/c5rz37wlpjddqpmthp3dlgcpqap4tjp7gah53ngg3v3clqnewuwd.py
# Topologically Sorted Source Nodes: [input_13], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_13 => cat_7
# Graph fragment:
#   %cat_7 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_23, %relu_24], 1), kwargs = {})
triton_poi_fused_cat_21 = async_compile.triton('triton_poi_fused_cat_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_21(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = (xindex // 512)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((256*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 512, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((256*x1) + ((-256) + x0)), tmp12, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-256) + x0), tmp12, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mg/cmgqvp7lrzfft3pjfzc6h6aiqy7lluxn5tlkddijz66oiza6wngk.py
# Topologically Sorted Source Nodes: [input_15, input_16, input_17], Original ATen: [aten.convolution, aten.relu, aten.mean]
# Source node to ATen node mapping:
#   input_15 => convolution_25
#   input_16 => relu_25
#   input_17 => mean
# Graph fragment:
#   %convolution_25 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_7, %primals_52, %primals_53, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_25 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_25,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%relu_25, [-1, -2], True), kwargs = {})
triton_per_fused_convolution_mean_relu_22 = async_compile.triton('triton_per_fused_convolution_mean_relu_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[4096, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_mean_relu_22', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_convolution_mean_relu_22(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4000
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 1000
    x1 = (xindex // 1000)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (1000*r2) + (9000*x1)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp9 = 9.0
    tmp10 = tmp8 / tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/u7/cu7dpusjnm7yt3b5etrxmm4vvsl2ugywwulnxuijjlly4x34mfz4.py
# Topologically Sorted Source Nodes: [input_15, input_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   input_15 => convolution_25
#   input_16 => relu_25
# Graph fragment:
#   %convolution_25 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_7, %primals_52, %primals_53, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_25 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_25,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_25, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_23 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_23', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1000
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3q/c3qoii36do2jtam7m3jhlytuykkypmcqmr7twk4w5stkyslqhvdx.py
# Topologically Sorted Source Nodes: [conv2d_24, relu_24], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_24 => convolution_24
#   relu_24 => relu_24
# Graph fragment:
#   %convolution_24 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_22, %primals_50, %primals_51, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_24 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_24,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_24, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_24 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_24', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ll/cllrnxsks4gdn3w6tf5gxd2u2stzyjmsqql4o7rrn4l4ga7j2fmq.py
# Topologically Sorted Source Nodes: [conv2d_21, relu_21], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_21 => convolution_21
#   relu_21 => relu_21
# Graph fragment:
#   %convolution_21 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_19, %primals_44, %primals_45, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_21 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_21,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_21, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_25 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_25(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hx/chx7vabdpspv3vt5tkxlo5dyphcmzc6xudpgw3n4harfu77ycpw5.py
# Topologically Sorted Source Nodes: [conv2d_18, relu_18], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_18 => convolution_18
#   relu_18 => relu_18
# Graph fragment:
#   %convolution_18 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_16, %primals_38, %primals_39, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_18 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_18,), kwargs = {})
#   %le_7 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_18, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_26 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_26', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_26(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 37632
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/t2/ct22ibtznsxt52xgmjekx6wdrldib6bgixp2xemxlkpijdgasznb.py
# Topologically Sorted Source Nodes: [conv2d_12, relu_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   relu_12 => relu_12
# Graph fragment:
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_10, %primals_26, %primals_27, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {})
#   %le_13 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_12, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_27 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_27', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_27', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_27(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vf/cvfcywcoir6zz63sb5ykrqafgtqsyn4rbib4xawb7ugv3tk5pirj.py
# Topologically Sorted Source Nodes: [conv2d_9, relu_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_9 => convolution_9
#   relu_9 => relu_9
# Graph fragment:
#   %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_20, %primals_21, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
#   %le_16 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_28 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_28', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_28', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_28(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100352
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/o6/co6j4d6e2424ff7a23ugfqqsp3f7dkkmpauq2qrab36olqyt25hs.py
# Topologically Sorted Source Nodes: [conv2d_6, relu_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   relu_6 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
#   %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_29 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_29', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_29', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_29(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53 = args
    args.clear()
    assert_size_stride(primals_1, (96, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_2, (96, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 96, 1, 1), (96, 1, 1, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (64, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (16, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_11, (16, ), (1, ))
    assert_size_stride(primals_12, (64, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (64, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (32, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_17, (32, ), (1, ))
    assert_size_stride(primals_18, (128, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (32, ), (1, ))
    assert_size_stride(primals_24, (128, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (128, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_27, (128, ), (1, ))
    assert_size_stride(primals_28, (48, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_29, (48, ), (1, ))
    assert_size_stride(primals_30, (192, 48, 1, 1), (48, 1, 1, 1))
    assert_size_stride(primals_31, (192, ), (1, ))
    assert_size_stride(primals_32, (192, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_33, (192, ), (1, ))
    assert_size_stride(primals_34, (48, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_35, (48, ), (1, ))
    assert_size_stride(primals_36, (192, 48, 1, 1), (48, 1, 1, 1))
    assert_size_stride(primals_37, (192, ), (1, ))
    assert_size_stride(primals_38, (192, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_39, (192, ), (1, ))
    assert_size_stride(primals_40, (64, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_41, (64, ), (1, ))
    assert_size_stride(primals_42, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_43, (256, ), (1, ))
    assert_size_stride(primals_44, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (256, ), (1, ))
    assert_size_stride(primals_46, (64, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_47, (64, ), (1, ))
    assert_size_stride(primals_48, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_49, (256, ), (1, ))
    assert_size_stride(primals_50, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_51, (256, ), (1, ))
    assert_size_stride(primals_52, (1000, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_53, (1000, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 7, 7), (147, 1, 21, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 288, 49, grid=grid(288, 49), 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, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_8, buf2, 1024, 9, grid=grid(1024, 9), stream=stream0)
        del primals_8
        buf3 = empty_strided_cuda((64, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_14, buf3, 1024, 9, grid=grid(1024, 9), stream=stream0)
        del primals_14
        buf4 = empty_strided_cuda((128, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_20, buf4, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_20
        buf5 = empty_strided_cuda((128, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_26, buf5, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_26
        buf6 = empty_strided_cuda((192, 48, 3, 3), (432, 1, 144, 48), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_32, buf6, 9216, 9, grid=grid(9216, 9), stream=stream0)
        del primals_32
        buf7 = empty_strided_cuda((192, 48, 3, 3), (432, 1, 144, 48), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_38, buf7, 9216, 9, grid=grid(9216, 9), stream=stream0)
        del primals_38
        buf8 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_44, buf8, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_44
        buf9 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_50, buf9, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_50
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 96, 29, 29), (80736, 1, 2784, 96))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf11, primals_2, 322944, grid=grid(322944), stream=stream0)
        del primals_2
        buf12 = empty_strided_cuda((4, 96, 14, 14), (18816, 1, 1344, 96), torch.float32)
        buf13 = empty_strided_cuda((4, 96, 14, 14), (18816, 1, 1344, 96), torch.int8)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf11, buf12, buf13, 75264, grid=grid(75264), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf12, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 16, 14, 14), (3136, 1, 224, 16))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf15, primals_5, 12544, grid=grid(12544), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 64, 14, 14), (12544, 1, 896, 64))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf15, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf18 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_9.run(buf16, primals_7, buf17, primals_9, buf18, 100352, grid=grid(100352), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 16, 14, 14), (3136, 1, 224, 16))
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, x_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf20, primals_11, 12544, grid=grid(12544), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf21 = extern_kernels.convolution(buf20, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 64, 14, 14), (12544, 1, 896, 64))
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf20, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf23 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128), torch.float32)
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.cat]
        triton_poi_fused_cat_9.run(buf21, primals_13, buf22, primals_15, buf23, 100352, grid=grid(100352), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 32, 14, 14), (6272, 1, 448, 32))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, x_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf25, primals_17, 25088, grid=grid(25088), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 14, 14), (25088, 1, 1792, 128))
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf27 = 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(buf27, (4, 128, 14, 14), (25088, 1, 1792, 128))
        buf28 = empty_strided_cuda((4, 256, 14, 14), (50176, 1, 3584, 256), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.cat]
        triton_poi_fused_cat_11.run(buf26, primals_19, buf27, primals_21, buf28, 200704, grid=grid(200704), stream=stream0)
        buf29 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256), torch.float32)
        buf30 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256), torch.int8)
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_12.run(buf28, buf29, buf30, 50176, grid=grid(50176), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf31 = extern_kernels.convolution(buf29, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 32, 7, 7), (1568, 1, 224, 32))
        buf32 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf32, primals_23, 6272, grid=grid(6272), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf33 = extern_kernels.convolution(buf32, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 128, 7, 7), (6272, 1, 896, 128))
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf32, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 128, 7, 7), (6272, 1, 896, 128))
        buf35 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256), torch.float32)
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.cat]
        triton_poi_fused_cat_14.run(buf33, primals_25, buf34, primals_27, buf35, 50176, grid=grid(50176), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, primals_28, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 48, 7, 7), (2352, 1, 336, 48))
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_13, x_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_15.run(buf37, primals_29, 9408, grid=grid(9408), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [conv2d_14], Original ATen: [aten.convolution]
        buf38 = extern_kernels.convolution(buf37, primals_30, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 192, 7, 7), (9408, 1, 1344, 192))
        # Topologically Sorted Source Nodes: [conv2d_15], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf37, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf40 = empty_strided_cuda((4, 384, 7, 7), (18816, 1, 2688, 384), torch.float32)
        # Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.cat]
        triton_poi_fused_cat_16.run(buf38, primals_31, buf39, primals_33, buf40, 75264, grid=grid(75264), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_16], Original ATen: [aten.convolution]
        buf41 = extern_kernels.convolution(buf40, primals_34, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 48, 7, 7), (2352, 1, 336, 48))
        buf42 = buf41; del buf41  # reuse
        # Topologically Sorted Source Nodes: [conv2d_16, x_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_15.run(buf42, primals_35, 9408, grid=grid(9408), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [conv2d_17], Original ATen: [aten.convolution]
        buf43 = extern_kernels.convolution(buf42, primals_36, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 192, 7, 7), (9408, 1, 1344, 192))
        # Topologically Sorted Source Nodes: [conv2d_18], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf42, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf45 = empty_strided_cuda((4, 384, 7, 7), (18816, 1, 2688, 384), torch.float32)
        # Topologically Sorted Source Nodes: [input_10], Original ATen: [aten.cat]
        triton_poi_fused_cat_16.run(buf43, primals_37, buf44, primals_39, buf45, 75264, grid=grid(75264), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_19], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_40, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 64, 7, 7), (3136, 1, 448, 64))
        buf47 = buf46; del buf46  # reuse
        # Topologically Sorted Source Nodes: [conv2d_19, x_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_17.run(buf47, primals_41, 12544, grid=grid(12544), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [conv2d_20], Original ATen: [aten.convolution]
        buf48 = extern_kernels.convolution(buf47, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 256, 7, 7), (12544, 1, 1792, 256))
        # Topologically Sorted Source Nodes: [conv2d_21], Original ATen: [aten.convolution]
        buf49 = extern_kernels.convolution(buf47, buf8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 256, 7, 7), (12544, 1, 1792, 256))
        buf50 = empty_strided_cuda((4, 512, 7, 7), (25088, 1, 3584, 512), torch.float32)
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.cat]
        triton_poi_fused_cat_18.run(buf48, primals_43, buf49, primals_45, buf50, 100352, grid=grid(100352), stream=stream0)
        buf51 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        buf52 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512), torch.int8)
        # Topologically Sorted Source Nodes: [input_12], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_19.run(buf50, buf51, buf52, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_22], Original ATen: [aten.convolution]
        buf53 = extern_kernels.convolution(buf51, primals_46, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 64, 3, 3), (576, 1, 192, 64))
        buf54 = buf53; del buf53  # reuse
        # Topologically Sorted Source Nodes: [conv2d_22, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_20.run(buf54, primals_47, 2304, grid=grid(2304), stream=stream0)
        del primals_47
        # Topologically Sorted Source Nodes: [conv2d_23], Original ATen: [aten.convolution]
        buf55 = extern_kernels.convolution(buf54, primals_48, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf55, (4, 256, 3, 3), (2304, 1, 768, 256))
        # Topologically Sorted Source Nodes: [conv2d_24], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf54, buf9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 256, 3, 3), (2304, 1, 768, 256))
        buf57 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Topologically Sorted Source Nodes: [input_13], Original ATen: [aten.cat]
        triton_poi_fused_cat_21.run(buf55, primals_49, buf56, primals_51, buf57, 18432, grid=grid(18432), stream=stream0)
        # Topologically Sorted Source Nodes: [input_15], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, primals_52, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 1000, 3, 3), (9000, 1, 3000, 1000))
        buf59 = empty_strided_cuda((4, 1000, 1, 1), (1000, 1, 4000, 4000), torch.float32)
        buf60 = reinterpret_tensor(buf59, (4, 1000, 1, 1), (1000, 1, 1, 1), 0); del buf59  # reuse
        # Topologically Sorted Source Nodes: [input_15, input_16, input_17], Original ATen: [aten.convolution, aten.relu, aten.mean]
        triton_per_fused_convolution_mean_relu_22.run(buf60, buf58, primals_53, 4000, 9, grid=grid(4000), stream=stream0)
        buf61 = empty_strided_cuda((4, 1000, 3, 3), (9000, 1, 3000, 1000), torch.bool)
        # Topologically Sorted Source Nodes: [input_15, input_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_23.run(buf58, primals_53, buf61, 36000, grid=grid(36000), stream=stream0)
        del buf58
        del primals_53
        buf62 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_24, relu_24], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_24.run(buf56, primals_51, buf62, 9216, grid=grid(9216), stream=stream0)
        del buf56
        del primals_51
        buf63 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_23, relu_23], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_24.run(buf55, primals_49, buf63, 9216, grid=grid(9216), stream=stream0)
        del buf55
        del primals_49
        buf64 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_21, relu_21], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_25.run(buf49, primals_45, buf64, 50176, grid=grid(50176), stream=stream0)
        del buf49
        del primals_45
        buf65 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_20, relu_20], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_25.run(buf48, primals_43, buf65, 50176, grid=grid(50176), stream=stream0)
        del buf48
        del primals_43
        buf66 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_18, relu_18], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_26.run(buf44, primals_39, buf66, 37632, grid=grid(37632), stream=stream0)
        del buf44
        del primals_39
        buf67 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_17, relu_17], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_26.run(buf43, primals_37, buf67, 37632, grid=grid(37632), stream=stream0)
        del buf43
        del primals_37
        buf68 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_15, relu_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_26.run(buf39, primals_33, buf68, 37632, grid=grid(37632), stream=stream0)
        del buf39
        del primals_33
        buf69 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_14, relu_14], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_26.run(buf38, primals_31, buf69, 37632, grid=grid(37632), stream=stream0)
        del buf38
        del primals_31
        buf70 = empty_strided_cuda((4, 128, 7, 7), (6272, 1, 896, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_12, relu_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_27.run(buf34, primals_27, buf70, 25088, grid=grid(25088), stream=stream0)
        del buf34
        del primals_27
        buf71 = empty_strided_cuda((4, 128, 7, 7), (6272, 1, 896, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_11, relu_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_27.run(buf33, primals_25, buf71, 25088, grid=grid(25088), stream=stream0)
        del buf33
        del primals_25
        buf72 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_9, relu_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_28.run(buf27, primals_21, buf72, 100352, grid=grid(100352), stream=stream0)
        del buf27
        del primals_21
        buf73 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_8, relu_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_28.run(buf26, primals_19, buf73, 100352, grid=grid(100352), stream=stream0)
        del buf26
        del primals_19
        buf74 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_6, relu_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_29.run(buf22, primals_15, buf74, 50176, grid=grid(50176), stream=stream0)
        del buf22
        del primals_15
        buf75 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_5, relu_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_29.run(buf21, primals_13, buf75, 50176, grid=grid(50176), stream=stream0)
        del buf21
        del primals_13
        buf76 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_3, relu_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_29.run(buf17, primals_9, buf76, 50176, grid=grid(50176), stream=stream0)
        del buf17
        del primals_9
        buf77 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_2, relu_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_29.run(buf16, primals_7, buf77, 50176, grid=grid(50176), stream=stream0)
        del buf16
        del primals_7
    return (buf60, buf0, buf1, primals_4, primals_6, buf2, primals_10, primals_12, buf3, primals_16, primals_18, buf4, primals_22, primals_24, buf5, primals_28, primals_30, buf6, primals_34, primals_36, buf7, primals_40, primals_42, buf8, primals_46, primals_48, buf9, primals_52, buf11, buf12, buf13, buf15, buf18, buf20, buf23, buf25, buf28, buf29, buf30, buf32, buf35, buf37, buf40, buf42, buf45, buf47, buf50, buf51, buf52, buf54, buf57, buf61, buf62, buf63, buf64, buf65, buf66, buf67, buf68, buf69, buf70, buf71, buf72, buf73, buf74, buf75, buf76, buf77, )


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((96, 3, 7, 7), (147, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((96, ), (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((16, 96, 1, 1), (96, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 16, 1, 1), (16, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 16, 1, 1), (16, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((32, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((32, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((48, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((48, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((192, 48, 1, 1), (48, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((192, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((192, 48, 3, 3), (432, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((192, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((48, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((48, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((192, 48, 1, 1), (48, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((192, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((192, 48, 3, 3), (432, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((192, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((64, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_41 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_42 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_43 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_44 = rand_strided((256, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_45 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_46 = rand_strided((64, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((256, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((1000, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53])
    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 copy
import torch
import torch.nn as nn
import torch.utils.data
from torchvision.models.squeezenet import squeezenet1_0
from torchvision.models.squeezenet import squeezenet1_1
import torch.nn.modules.activation


class GramMatrix(nn.Module):

    def forward(self, x):
        b, c, h, w = x.size()
        F = x.view(b, c, h * w)
        G = torch.bmm(F, F.transpose(1, 2))
        G.div_(h * w)
        return G


class GramDiag(nn.Module):
    """
    docstring for GramDiag
    """

    def __init__(self, gram_diagonal_squared=False):
        super().__init__()
        self.__gram_diagonal_squared = gram_diagonal_squared

    def forward(self, x):
        b, c, h, w = x.size()
        x = x.view(b, c, 1, h * w)
        gram_diag = None
        for b in range(x.size(0)):
            if self.__gram_diagonal_squared:
                z = torch.bmm(x[b] * x[b], (x[b] * x[b]).transpose(2, 1))
            else:
                z = torch.bmm(x[b], x[b].transpose(2, 1))
            if isinstance(gram_diag, torch.Tensor):
                gram_diag = torch.cat(gram_diag, z)
            else:
                gram_diag = z
        gram_diag = torch.squeeze(gram_diag).unsqueeze(0)
        return gram_diag.div_(h * w)


class SqueezeNet(nn.Module):

    def __init__(self, version=1.0, num_classes=1000, pretrained=False,
        layer='', gram=False, gram_diag=False, gram_diagonal_squared=False):
        super().__init__()
        if version not in [1.0, 1.1]:
            raise ValueError(
                'Unsupported SqueezeNet version {version}:1.0 or 1.1 expected'
                .format(version=version))
        self.num_classes = num_classes
        if version == 1.0:
            pytorch_squeeze = squeezenet1_0(pretrained, num_classes=num_classes
                )
            features_names = ['conv_1', 'relu_1', 'maxpool_1', 'fire_2',
                'fire_3', 'fire_4', 'maxpool_4', 'fire_5', 'fire_6',
                'fire_7', 'fire_8', 'maxpool_8', 'fire_9']
        else:
            pytorch_squeeze = squeezenet1_1(pretrained, num_classes=num_classes
                )
            features_names = ['conv_1', 'relu_1', 'maxpool_1', 'fire_2',
                'fire_3', 'maxpool_3', 'fire_4', 'fire_5', 'maxpool_5',
                'fire_6', 'fire_7', 'fire_8', 'fire_9']
        classifier_names = ['drop_10', 'conv_10', 'relu_10', 'avgpool_10']
        self.features = torch.nn.Sequential()
        for name, module in zip(features_names, pytorch_squeeze.features):
            self.features.add_module(name, copy.deepcopy(module))
            if layer is name:
                break
        if len(features_names) == len(self.features
            ) and layer != features_names[-1]:
            for name, module in zip(classifier_names, pytorch_squeeze.
                classifier):
                self.features.add_module(name, copy.deepcopy(module))
                if layer is name:
                    break
        del pytorch_squeeze
        if gram:
            self.features.add_module('gram matrix', GramMatrix())
        elif gram_diag:
            self.features.add_module('gram diagonal', GramDiag(
                gram_diagonal_squared))

    def forward(self, x):
        return self.features(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
import copy
import torch.nn as nn
import torch.utils.data
from torchvision.models.squeezenet import squeezenet1_0
from torchvision.models.squeezenet import squeezenet1_1
import torch.nn.modules.activation
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 288
    xnumel = 49
    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 + 49 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 147 * 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 % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 16 * x2 + 144 * 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 % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 32 * x2 + 288 * 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 % 48
    y1 = yindex // 48
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 48 * x2 + 432 * 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 % 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_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 322944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 96
    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_7(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 75264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 96
    x1 = xindex // 96 % 14
    x2 = xindex // 1344 % 14
    x3 = xindex // 18816
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 192 * x1 + 5568 * x2 + 80736 * x3), xmask)
    tmp1 = tl.load(in_ptr0 + (96 + x0 + 192 * x1 + 5568 * x2 + 80736 * x3),
        xmask)
    tmp3 = tl.load(in_ptr0 + (192 + x0 + 192 * x1 + 5568 * x2 + 80736 * x3),
        xmask)
    tmp5 = tl.load(in_ptr0 + (2784 + x0 + 192 * x1 + 5568 * x2 + 80736 * x3
        ), xmask)
    tmp7 = tl.load(in_ptr0 + (2880 + x0 + 192 * x1 + 5568 * x2 + 80736 * x3
        ), xmask)
    tmp9 = tl.load(in_ptr0 + (2976 + x0 + 192 * x1 + 5568 * x2 + 80736 * x3
        ), xmask)
    tmp11 = tl.load(in_ptr0 + (5568 + x0 + 192 * x1 + 5568 * x2 + 80736 *
        x3), xmask)
    tmp13 = tl.load(in_ptr0 + (5664 + x0 + 192 * x1 + 5568 * x2 + 80736 *
        x3), xmask)
    tmp15 = tl.load(in_ptr0 + (5760 + x0 + 192 * x1 + 5568 * x2 + 80736 *
        x3), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + x4, tmp16, xmask)
    tl.store(out_ptr1 + x4, tmp41, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 12544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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_cat_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-64 + x0), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, None)


@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_cat_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = xindex // 256
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (128 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp15 = tl.load(in_ptr2 + (128 * x1 + (-128 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-128 + x0), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 1792 % 7
    x1 = xindex // 256 % 7
    x0 = xindex % 256
    x5 = xindex // 1792
    x6 = xindex
    tmp0 = 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 14, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (x0 + 512 * x1 + 7168 * x5), tmp10 & xmask,
        other=float('-inf'))
    tmp12 = 1 + 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 7168 * x5), tmp16 &
        xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 + 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (512 + x0 + 512 * x1 + 7168 * x5), tmp23 &
        xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 1 + 2 * x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (3584 + x0 + 512 * x1 + 7168 * x5), tmp30 &
        xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (3840 + x0 + 512 * x1 + 7168 * x5), tmp33 &
        xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (4096 + x0 + 512 * x1 + 7168 * x5), tmp36 &
        xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 2 + 2 * x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (7168 + x0 + 512 * x1 + 7168 * x5), tmp43 &
        xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (7424 + x0 + 512 * x1 + 7168 * x5), tmp46 &
        xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (7680 + x0 + 512 * x1 + 7168 * x5), 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_13(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 6272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_cat_14(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x1 = xindex // 256
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (128 * x1 + x0), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp15 = tl.load(in_ptr2 + (128 * x1 + (-128 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-128 + x0), tmp12 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_15(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 9408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 48
    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_cat_16(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 75264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 384
    x1 = xindex // 384
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 192, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (192 * x1 + x0), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 384, tl.int64)
    tmp15 = tl.load(in_ptr2 + (192 * x1 + (-192 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-192 + x0), tmp12 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_17(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 12544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_cat_18(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = xindex // 512
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (256 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp15 = tl.load(in_ptr2 + (256 * x1 + (-256 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-256 + x0), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_19(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = xindex // 512 % 3
    x2 = xindex // 1536 % 3
    x3 = xindex // 4608
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 7168 * x2 + 25088 * x3), None)
    tmp1 = tl.load(in_ptr0 + (512 + x0 + 1024 * x1 + 7168 * x2 + 25088 * x3
        ), None)
    tmp3 = tl.load(in_ptr0 + (1024 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp5 = tl.load(in_ptr0 + (3584 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp7 = tl.load(in_ptr0 + (4096 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp9 = tl.load(in_ptr0 + (4608 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp11 = tl.load(in_ptr0 + (7168 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp13 = tl.load(in_ptr0 + (7680 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp15 = tl.load(in_ptr0 + (8192 + x0 + 1024 * x1 + 7168 * x2 + 25088 *
        x3), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + x4, tmp16, None)
    tl.store(out_ptr1 + x4, tmp41, None)


@triton.jit
def triton_poi_fused_convolution_relu_20(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_cat_21(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = xindex // 512
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (256 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp15 = tl.load(in_ptr2 + (256 * x1 + (-256 + x0)), tmp12,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-256 + x0), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, None)


@triton.jit
def triton_per_fused_convolution_mean_relu_22(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4000
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 1000
    x1 = xindex // 1000
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 1000 * r2 + 9000 * x1), rmask & xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp9 = 9.0
    tmp10 = tmp8 / tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1000
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_25(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_26(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 37632
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_27(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_28(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_29(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52, primals_53
        ) = args
    args.clear()
    assert_size_stride(primals_1, (96, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_2, (96,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 96, 1, 1), (96, 1, 1, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (64, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (16, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (64, 16, 1, 1), (16, 1, 1, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (64, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (32, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_17, (32,), (1,))
    assert_size_stride(primals_18, (128, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (32, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_23, (32,), (1,))
    assert_size_stride(primals_24, (128, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (128, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (48, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_29, (48,), (1,))
    assert_size_stride(primals_30, (192, 48, 1, 1), (48, 1, 1, 1))
    assert_size_stride(primals_31, (192,), (1,))
    assert_size_stride(primals_32, (192, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_33, (192,), (1,))
    assert_size_stride(primals_34, (48, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_35, (48,), (1,))
    assert_size_stride(primals_36, (192, 48, 1, 1), (48, 1, 1, 1))
    assert_size_stride(primals_37, (192,), (1,))
    assert_size_stride(primals_38, (192, 48, 3, 3), (432, 9, 3, 1))
    assert_size_stride(primals_39, (192,), (1,))
    assert_size_stride(primals_40, (64, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_41, (64,), (1,))
    assert_size_stride(primals_42, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_43, (256,), (1,))
    assert_size_stride(primals_44, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_45, (256,), (1,))
    assert_size_stride(primals_46, (64, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_47, (64,), (1,))
    assert_size_stride(primals_48, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_49, (256,), (1,))
    assert_size_stride(primals_50, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_51, (256,), (1,))
    assert_size_stride(primals_52, (1000, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_53, (1000,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 7, 7), (147, 1, 21, 3), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_0[grid(288, 49)](primals_1, buf0, 288, 49, XBLOCK=
            32, YBLOCK=32, 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, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_2[grid(1024, 9)](primals_8, buf2, 1024, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf3 = empty_strided_cuda((64, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_2[grid(1024, 9)](primals_14, buf3, 1024, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf4 = empty_strided_cuda((128, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_3[grid(4096, 9)](primals_20, buf4, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_20
        buf5 = empty_strided_cuda((128, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_3[grid(4096, 9)](primals_26, buf5, 4096, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_26
        buf6 = empty_strided_cuda((192, 48, 3, 3), (432, 1, 144, 48), torch
            .float32)
        triton_poi_fused_4[grid(9216, 9)](primals_32, buf6, 9216, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_32
        buf7 = empty_strided_cuda((192, 48, 3, 3), (432, 1, 144, 48), torch
            .float32)
        triton_poi_fused_4[grid(9216, 9)](primals_38, buf7, 9216, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_38
        buf8 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_5[grid(16384, 9)](primals_44, buf8, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_44
        buf9 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_5[grid(16384, 9)](primals_50, buf9, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_50
        buf10 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 96, 29, 29), (80736, 1, 2784, 96))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_6[grid(322944)](buf11, primals_2,
            322944, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf12 = empty_strided_cuda((4, 96, 14, 14), (18816, 1, 1344, 96),
            torch.float32)
        buf13 = empty_strided_cuda((4, 96, 14, 14), (18816, 1, 1344, 96),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_7[grid(75264)](buf11,
            buf12, buf13, 75264, XBLOCK=512, num_warps=8, num_stages=1)
        buf14 = extern_kernels.convolution(buf12, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 16, 14, 14), (3136, 1, 224, 16))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_8[grid(12544)](buf15, primals_5, 
            12544, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf16 = extern_kernels.convolution(buf15, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf17 = extern_kernels.convolution(buf15, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf18 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128),
            torch.float32)
        triton_poi_fused_cat_9[grid(100352)](buf16, primals_7, buf17,
            primals_9, buf18, 100352, XBLOCK=512, num_warps=8, num_stages=1)
        buf19 = extern_kernels.convolution(buf18, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 16, 14, 14), (3136, 1, 224, 16))
        buf20 = buf19
        del buf19
        triton_poi_fused_convolution_relu_8[grid(12544)](buf20, primals_11,
            12544, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf21 = extern_kernels.convolution(buf20, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf22 = extern_kernels.convolution(buf20, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 14, 14), (12544, 1, 896, 64))
        buf23 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128),
            torch.float32)
        triton_poi_fused_cat_9[grid(100352)](buf21, primals_13, buf22,
            primals_15, buf23, 100352, XBLOCK=512, num_warps=8, num_stages=1)
        buf24 = extern_kernels.convolution(buf23, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 32, 14, 14), (6272, 1, 448, 32))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_10[grid(25088)](buf25, primals_17,
            25088, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf26 = extern_kernels.convolution(buf25, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 14, 14), (25088, 1, 1792, 128))
        buf27 = 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(buf27, (4, 128, 14, 14), (25088, 1, 1792, 128))
        buf28 = empty_strided_cuda((4, 256, 14, 14), (50176, 1, 3584, 256),
            torch.float32)
        triton_poi_fused_cat_11[grid(200704)](buf26, primals_19, buf27,
            primals_21, buf28, 200704, XBLOCK=512, num_warps=8, num_stages=1)
        buf29 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256),
            torch.float32)
        buf30 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_12[grid(50176)](buf28,
            buf29, buf30, 50176, XBLOCK=256, num_warps=4, num_stages=1)
        buf31 = extern_kernels.convolution(buf29, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf31, (4, 32, 7, 7), (1568, 1, 224, 32))
        buf32 = buf31
        del buf31
        triton_poi_fused_convolution_relu_13[grid(6272)](buf32, primals_23,
            6272, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_23
        buf33 = extern_kernels.convolution(buf32, primals_24, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 128, 7, 7), (6272, 1, 896, 128))
        buf34 = extern_kernels.convolution(buf32, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 128, 7, 7), (6272, 1, 896, 128))
        buf35 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256),
            torch.float32)
        triton_poi_fused_cat_14[grid(50176)](buf33, primals_25, buf34,
            primals_27, buf35, 50176, XBLOCK=512, num_warps=4, num_stages=1)
        buf36 = extern_kernels.convolution(buf35, primals_28, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 48, 7, 7), (2352, 1, 336, 48))
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_15[grid(9408)](buf37, primals_29,
            9408, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_29
        buf38 = extern_kernels.convolution(buf37, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf39 = extern_kernels.convolution(buf37, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf40 = empty_strided_cuda((4, 384, 7, 7), (18816, 1, 2688, 384),
            torch.float32)
        triton_poi_fused_cat_16[grid(75264)](buf38, primals_31, buf39,
            primals_33, buf40, 75264, XBLOCK=512, num_warps=8, num_stages=1)
        buf41 = extern_kernels.convolution(buf40, primals_34, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 48, 7, 7), (2352, 1, 336, 48))
        buf42 = buf41
        del buf41
        triton_poi_fused_convolution_relu_15[grid(9408)](buf42, primals_35,
            9408, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_35
        buf43 = extern_kernels.convolution(buf42, primals_36, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf44 = extern_kernels.convolution(buf42, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 192, 7, 7), (9408, 1, 1344, 192))
        buf45 = empty_strided_cuda((4, 384, 7, 7), (18816, 1, 2688, 384),
            torch.float32)
        triton_poi_fused_cat_16[grid(75264)](buf43, primals_37, buf44,
            primals_39, buf45, 75264, XBLOCK=512, num_warps=8, num_stages=1)
        buf46 = extern_kernels.convolution(buf45, primals_40, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 64, 7, 7), (3136, 1, 448, 64))
        buf47 = buf46
        del buf46
        triton_poi_fused_convolution_relu_17[grid(12544)](buf47, primals_41,
            12544, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_41
        buf48 = extern_kernels.convolution(buf47, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf48, (4, 256, 7, 7), (12544, 1, 1792, 256))
        buf49 = extern_kernels.convolution(buf47, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 256, 7, 7), (12544, 1, 1792, 256))
        buf50 = empty_strided_cuda((4, 512, 7, 7), (25088, 1, 3584, 512),
            torch.float32)
        triton_poi_fused_cat_18[grid(100352)](buf48, primals_43, buf49,
            primals_45, buf50, 100352, XBLOCK=512, num_warps=8, num_stages=1)
        buf51 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        buf52 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_19[grid(18432)](buf50,
            buf51, buf52, 18432, XBLOCK=256, num_warps=4, num_stages=1)
        buf53 = extern_kernels.convolution(buf51, primals_46, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 64, 3, 3), (576, 1, 192, 64))
        buf54 = buf53
        del buf53
        triton_poi_fused_convolution_relu_20[grid(2304)](buf54, primals_47,
            2304, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_47
        buf55 = extern_kernels.convolution(buf54, primals_48, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf55, (4, 256, 3, 3), (2304, 1, 768, 256))
        buf56 = extern_kernels.convolution(buf54, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 256, 3, 3), (2304, 1, 768, 256))
        buf57 = empty_strided_cuda((4, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_cat_21[grid(18432)](buf55, primals_49, buf56,
            primals_51, buf57, 18432, XBLOCK=256, num_warps=4, num_stages=1)
        buf58 = extern_kernels.convolution(buf57, primals_52, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 1000, 3, 3), (9000, 1, 3000, 1000))
        buf59 = empty_strided_cuda((4, 1000, 1, 1), (1000, 1, 4000, 4000),
            torch.float32)
        buf60 = reinterpret_tensor(buf59, (4, 1000, 1, 1), (1000, 1, 1, 1), 0)
        del buf59
        triton_per_fused_convolution_mean_relu_22[grid(4000)](buf60, buf58,
            primals_53, 4000, 9, XBLOCK=32, num_warps=4, num_stages=1)
        buf61 = empty_strided_cuda((4, 1000, 3, 3), (9000, 1, 3000, 1000),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_23[grid(36000)](
            buf58, primals_53, buf61, 36000, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf58
        del primals_53
        buf62 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_24[grid(9216)](
            buf56, primals_51, buf62, 9216, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf56
        del primals_51
        buf63 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_24[grid(9216)](
            buf55, primals_49, buf63, 9216, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf55
        del primals_49
        buf64 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_25[grid(50176)](
            buf49, primals_45, buf64, 50176, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf49
        del primals_45
        buf65 = empty_strided_cuda((4, 256, 7, 7), (12544, 1, 1792, 256),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_25[grid(50176)](
            buf48, primals_43, buf65, 50176, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf48
        del primals_43
        buf66 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_26[grid(37632)](
            buf44, primals_39, buf66, 37632, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf44
        del primals_39
        buf67 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_26[grid(37632)](
            buf43, primals_37, buf67, 37632, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf43
        del primals_37
        buf68 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_26[grid(37632)](
            buf39, primals_33, buf68, 37632, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf39
        del primals_33
        buf69 = empty_strided_cuda((4, 192, 7, 7), (9408, 1, 1344, 192),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_26[grid(37632)](
            buf38, primals_31, buf69, 37632, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf38
        del primals_31
        buf70 = empty_strided_cuda((4, 128, 7, 7), (6272, 1, 896, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_27[grid(25088)](
            buf34, primals_27, buf70, 25088, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf34
        del primals_27
        buf71 = empty_strided_cuda((4, 128, 7, 7), (6272, 1, 896, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_27[grid(25088)](
            buf33, primals_25, buf71, 25088, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf33
        del primals_25
        buf72 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_28[grid(100352)](
            buf27, primals_21, buf72, 100352, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf27
        del primals_21
        buf73 = empty_strided_cuda((4, 128, 14, 14), (25088, 1, 1792, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_28[grid(100352)](
            buf26, primals_19, buf73, 100352, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf26
        del primals_19
        buf74 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_29[grid(50176)](
            buf22, primals_15, buf74, 50176, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf22
        del primals_15
        buf75 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_29[grid(50176)](
            buf21, primals_13, buf75, 50176, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf21
        del primals_13
        buf76 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_29[grid(50176)](
            buf17, primals_9, buf76, 50176, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf17
        del primals_9
        buf77 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_29[grid(50176)](
            buf16, primals_7, buf77, 50176, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf16
        del primals_7
    return (buf60, buf0, buf1, primals_4, primals_6, buf2, primals_10,
        primals_12, buf3, primals_16, primals_18, buf4, primals_22,
        primals_24, buf5, primals_28, primals_30, buf6, primals_34,
        primals_36, buf7, primals_40, primals_42, buf8, primals_46,
        primals_48, buf9, primals_52, buf11, buf12, buf13, buf15, buf18,
        buf20, buf23, buf25, buf28, buf29, buf30, buf32, buf35, buf37,
        buf40, buf42, buf45, buf47, buf50, buf51, buf52, buf54, buf57,
        buf61, buf62, buf63, buf64, buf65, buf66, buf67, buf68, buf69,
        buf70, buf71, buf72, buf73, buf74, buf75, buf76, buf77)


class GramMatrix(nn.Module):

    def forward(self, x):
        b, c, h, w = x.size()
        F = x.view(b, c, h * w)
        G = torch.bmm(F, F.transpose(1, 2))
        G.div_(h * w)
        return G


class GramDiag(nn.Module):
    """
    docstring for GramDiag
    """

    def __init__(self, gram_diagonal_squared=False):
        super().__init__()
        self.__gram_diagonal_squared = gram_diagonal_squared

    def forward(self, x):
        b, c, h, w = x.size()
        x = x.view(b, c, 1, h * w)
        gram_diag = None
        for b in range(x.size(0)):
            if self.__gram_diagonal_squared:
                z = torch.bmm(x[b] * x[b], (x[b] * x[b]).transpose(2, 1))
            else:
                z = torch.bmm(x[b], x[b].transpose(2, 1))
            if isinstance(gram_diag, torch.Tensor):
                gram_diag = torch.cat(gram_diag, z)
            else:
                gram_diag = z
        gram_diag = torch.squeeze(gram_diag).unsqueeze(0)
        return gram_diag.div_(h * w)


class SqueezeNetNew(nn.Module):

    def __init__(self, version=1.0, num_classes=1000, pretrained=False,
        layer='', gram=False, gram_diag=False, gram_diagonal_squared=False):
        super().__init__()
        if version not in [1.0, 1.1]:
            raise ValueError(
                'Unsupported SqueezeNet version {version}:1.0 or 1.1 expected'
                .format(version=version))
        self.num_classes = num_classes
        if version == 1.0:
            pytorch_squeeze = squeezenet1_0(pretrained, num_classes=num_classes
                )
            features_names = ['conv_1', 'relu_1', 'maxpool_1', 'fire_2',
                'fire_3', 'fire_4', 'maxpool_4', 'fire_5', 'fire_6',
                'fire_7', 'fire_8', 'maxpool_8', 'fire_9']
        else:
            pytorch_squeeze = squeezenet1_1(pretrained, num_classes=num_classes
                )
            features_names = ['conv_1', 'relu_1', 'maxpool_1', 'fire_2',
                'fire_3', 'maxpool_3', 'fire_4', 'fire_5', 'maxpool_5',
                'fire_6', 'fire_7', 'fire_8', 'fire_9']
        classifier_names = ['drop_10', 'conv_10', 'relu_10', 'avgpool_10']
        self.features = torch.nn.Sequential()
        for name, module in zip(features_names, pytorch_squeeze.features):
            self.features.add_module(name, copy.deepcopy(module))
            if layer is name:
                break
        if len(features_names) == len(self.features
            ) and layer != features_names[-1]:
            for name, module in zip(classifier_names, pytorch_squeeze.
                classifier):
                self.features.add_module(name, copy.deepcopy(module))
                if layer is name:
                    break
        del pytorch_squeeze
        if gram:
            self.features.add_module('gram matrix', GramMatrix())
        elif gram_diag:
            self.features.add_module('gram diagonal', GramDiag(
                gram_diagonal_squared))

    def forward(self, input_0):
        primals_1 = self.features.conv_1.weight
        primals_2 = self.features.conv_1.bias
        primals_4 = self.features.fire_2.squeeze.weight
        primals_5 = self.features.fire_2.squeeze.bias
        primals_6 = self.features.fire_2.expand1x1.weight
        primals_7 = self.features.fire_2.expand1x1.bias
        primals_8 = self.features.fire_2.expand3x3.weight
        primals_9 = self.features.fire_2.expand3x3.bias
        primals_10 = self.features.fire_3.squeeze.weight
        primals_11 = self.features.fire_3.squeeze.bias
        primals_12 = self.features.fire_3.expand1x1.weight
        primals_13 = self.features.fire_3.expand1x1.bias
        primals_14 = self.features.fire_3.expand3x3.weight
        primals_15 = self.features.fire_3.expand3x3.bias
        primals_16 = self.features.fire_4.squeeze.weight
        primals_17 = self.features.fire_4.squeeze.bias
        primals_18 = self.features.fire_4.expand1x1.weight
        primals_19 = self.features.fire_4.expand1x1.bias
        primals_20 = self.features.fire_4.expand3x3.weight
        primals_21 = self.features.fire_4.expand3x3.bias
        primals_22 = self.features.fire_5.squeeze.weight
        primals_23 = self.features.fire_5.squeeze.bias
        primals_24 = self.features.fire_5.expand1x1.weight
        primals_25 = self.features.fire_5.expand1x1.bias
        primals_26 = self.features.fire_5.expand3x3.weight
        primals_27 = self.features.fire_5.expand3x3.bias
        primals_28 = self.features.fire_6.squeeze.weight
        primals_29 = self.features.fire_6.squeeze.bias
        primals_30 = self.features.fire_6.expand1x1.weight
        primals_31 = self.features.fire_6.expand1x1.bias
        primals_32 = self.features.fire_6.expand3x3.weight
        primals_33 = self.features.fire_6.expand3x3.bias
        primals_34 = self.features.fire_7.squeeze.weight
        primals_35 = self.features.fire_7.squeeze.bias
        primals_36 = self.features.fire_7.expand1x1.weight
        primals_37 = self.features.fire_7.expand1x1.bias
        primals_38 = self.features.fire_7.expand3x3.weight
        primals_39 = self.features.fire_7.expand3x3.bias
        primals_40 = self.features.fire_8.squeeze.weight
        primals_41 = self.features.fire_8.squeeze.bias
        primals_42 = self.features.fire_8.expand1x1.weight
        primals_43 = self.features.fire_8.expand1x1.bias
        primals_44 = self.features.fire_8.expand3x3.weight
        primals_45 = self.features.fire_8.expand3x3.bias
        primals_46 = self.features.fire_9.squeeze.weight
        primals_47 = self.features.fire_9.squeeze.bias
        primals_48 = self.features.fire_9.expand1x1.weight
        primals_49 = self.features.fire_9.expand1x1.bias
        primals_50 = self.features.fire_9.expand3x3.weight
        primals_51 = self.features.fire_9.expand3x3.bias
        primals_52 = self.features.conv_10.weight
        primals_53 = self.features.conv_10.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53])
        return output[0]
