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

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


# kernel path: runs/run_shard_7/inductor_cache/tl/ctltp4h4vhls3wzf43ymi74qp2do7ftk2ehxt6m24dymf5mgdd77.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, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 288
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (27*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/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_7/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_7/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_7/inductor_cache/i5/ci5m7nhckffd3gacyfsu5iumr7vpbe4uy62eh3okvpyt7343vo33.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 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_7/inductor_cache/yl/cylsyrzru64h3777bghq4brfo5xznorpgywpstgksyzpqwzecdey.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=[262144, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 262144
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (512*x2) + (4608*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dk/cdkkkspxwdoe6btostfo6vxtwdvqjw67uh2giokg6n3wyx6vy46t.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_6 = async_compile.triton('triton_poi_fused_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 131072
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/j3/cj3ckfwoqki66hpfbfxqzpgkaienmantfbb2m5xbqhmslrl3xvux.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_7 = async_compile.triton('triton_poi_fused_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5d/c5dn3vfx27sppahvv4gh7on42hsg4tvbxk47y22ldg5vy32vccyk.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_8 = async_compile.triton('triton_poi_fused_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ci/ccicanokazgrzj2bcusdbw4c2kysvmnfit3htkewlamnx4yjs2la.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_9 = async_compile.triton('triton_poi_fused_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gv/cgvp355dqlzl5utvtw7vcp7njsyqhh6mn274ao5xhdba4pf4dusl.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_10 = async_compile.triton('triton_poi_fused_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, 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_10', '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_10(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 24576
    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 % 96
    y1 = (yindex // 96)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (96*x2) + (864*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uy/cuyiwkcialetr2crn22msxjnh574lkfjpujebhnqv3hsfiv2wycd.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_11 = async_compile.triton('triton_poi_fused_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=[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_11', '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_11(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 % 96
    y1 = (yindex // 96)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (96*x2) + (864*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/y2/cy2oitmmbtyj62rstido4jbhwpqibnjz5iwlf3w72ydb3ygrrkag.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_12 = async_compile.triton('triton_poi_fused_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, 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_12', '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_12(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 36864
    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 % 192
    y1 = (yindex // 192)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (192*x2) + (1728*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6u/c6uikbuyqjkdyl4ymjkzksengge5z2iflkp36ngvhmah4hqbpcwd.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_13 = async_compile.triton('triton_poi_fused_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=[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_13', '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_13(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 768
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (4*x2) + (36*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/24/c24ckhl4mmgczytxldzozhpqwumaiwljo4foqev6o6cgv67gto3l.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_convolution_elu_14 = async_compile.triton('triton_poi_fused_convolution_elu_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_elu_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 393216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 96
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/no/cno5mxcarkc5fkgv6czlqdrydupoyqur7ztxybkncqhalmm2tf43.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_15 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_15(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 98304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 96
    x1 = (xindex // 96) % 16
    x2 = (xindex // 1536)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (192*x1) + (6144*x2)), None)
    tmp1 = tl.load(in_ptr0 + (96 + x0 + (192*x1) + (6144*x2)), None)
    tmp3 = tl.load(in_ptr0 + (3072 + x0 + (192*x1) + (6144*x2)), None)
    tmp5 = tl.load(in_ptr0 + (3168 + x0 + (192*x1) + (6144*x2)), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tf/ctfdmlli5ya2x3ufcn6vnv73za2dctni463o4lujtdztgc2zzc3l.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_3 => convolution_1
#   x_4 => expm1_1, gt_1, mul_3, mul_5, where_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = 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 = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_convolution_elu_16 = async_compile.triton('triton_poi_fused_convolution_elu_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=[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_elu_16', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_elu_16(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jf/cjfrhycm77mdhfgxtwmkhngn5bt3rjdh4laqr463bkp454bgvkje.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.cat, aten.elu]
# Source node to ATen node mapping:
#   out => cat
#   out_1 => expm1_2, gt_2, mul_6, mul_8, where_2
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_2, %convolution_3], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%cat, 0), kwargs = {})
#   %mul_6 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat, 1.0), kwargs = {})
#   %expm1_2 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_6,), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_2, 1.0), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mul_6, %mul_8), kwargs = {})
triton_poi_fused_cat_elu_17 = async_compile.triton('triton_poi_fused_cat_elu_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=[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_elu_17', '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_elu_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 128, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + ((64*x1) + ((-64) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + ((-64) + x0), tmp10, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + (x2), tmp25, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vz/cvzuhlrfbl3xinkoms4oztxhw6yvrfs4672lpxlczlvloujitwi2.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_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_18 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[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_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_max_pool2d_with_indices_18(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128) % 8
    x2 = (xindex // 1024)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x1) + (4096*x2)), None)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + (256*x1) + (4096*x2)), None)
    tmp3 = tl.load(in_ptr0 + (2048 + x0 + (256*x1) + (4096*x2)), None)
    tmp5 = tl.load(in_ptr0 + (2176 + x0 + (256*x1) + (4096*x2)), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ie/cienvcboqehmmybcnm2ak3fgf2wfcprma2afozjmalwntkzpnkcc.py
# Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_8 => convolution_7
#   x_9 => expm1_5, gt_5, mul_15, mul_17, where_5
# Graph fragment:
#   %convolution_7 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_7, 0), kwargs = {})
#   %mul_15 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_7, 1.0), kwargs = {})
#   %expm1_5 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_15,), kwargs = {})
#   %mul_17 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_5, 1.0), kwargs = {})
#   %where_5 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %mul_15, %mul_17), kwargs = {})
triton_poi_fused_convolution_elu_19 = async_compile.triton('triton_poi_fused_convolution_elu_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=[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_elu_19', '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_elu_19(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/s7/cs72op2wseayy7rw5pp5ux2g432mr35e4t3ugxnw5eqrtw2gm4w3.py
# Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.cat, aten.elu]
# Source node to ATen node mapping:
#   out_4 => cat_2
#   out_5 => expm1_6, gt_6, mul_18, mul_20, where_6
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_8, %convolution_9], 1), kwargs = {})
#   %gt_6 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%cat_2, 0), kwargs = {})
#   %mul_18 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat_2, 1.0), kwargs = {})
#   %expm1_6 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_18,), kwargs = {})
#   %mul_20 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_6, 1.0), kwargs = {})
#   %where_6 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %mul_18, %mul_20), kwargs = {})
triton_poi_fused_cat_elu_20 = async_compile.triton('triton_poi_fused_cat_elu_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=[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_elu_20', '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_elu_20(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 256, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + ((128*x1) + ((-128) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + ((-128) + x0), tmp10, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + (x2), tmp25, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mr/cmradow7iwzoqdrysk6v2wesa5zprwdvpydypeervvhsyltqre72.py
# Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_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_21 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[16384], 
    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_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_max_pool2d_with_indices_21(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = (xindex // 256) % 4
    x2 = (xindex // 1024)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (512*x1) + (4096*x2)), None)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + (512*x1) + (4096*x2)), None)
    tmp3 = tl.load(in_ptr0 + (2048 + x0 + (512*x1) + (4096*x2)), None)
    tmp5 = tl.load(in_ptr0 + (2304 + x0 + (512*x1) + (4096*x2)), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x3), tmp6, None)
    tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/f7/cf7yd5d3a5faobcccdehkhd2jwfsrajgfqkex6kvk2re4umjxv4w.py
# Topologically Sorted Source Nodes: [x_13, x_14], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_13 => convolution_13
#   x_14 => expm1_9, gt_9, mul_27, mul_29, where_9
# Graph fragment:
#   %convolution_13 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_28, %primals_29, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_9 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_13, 0), kwargs = {})
#   %mul_27 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_13, 1.0), kwargs = {})
#   %expm1_9 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_27,), kwargs = {})
#   %mul_29 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_9, 1.0), kwargs = {})
#   %where_9 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_9, %mul_27, %mul_29), kwargs = {})
triton_poi_fused_convolution_elu_22 = async_compile.triton('triton_poi_fused_convolution_elu_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_elu_22', '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_elu_22(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4d/c4d7teeu3x6lkpzfzvpaqadgozguv6nqedv4lj2olz4alwro6c6l.py
# Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.cat, aten.elu]
# Source node to ATen node mapping:
#   out_8 => cat_4
#   out_9 => expm1_10, gt_10, mul_30, mul_32, where_10
# Graph fragment:
#   %cat_4 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution_14, %convolution_15], 1), kwargs = {})
#   %gt_10 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%cat_4, 0), kwargs = {})
#   %mul_30 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat_4, 1.0), kwargs = {})
#   %expm1_10 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_30,), kwargs = {})
#   %mul_32 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_10, 1.0), kwargs = {})
#   %where_10 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_10, %mul_30, %mul_32), kwargs = {})
triton_poi_fused_cat_elu_23 = async_compile.triton('triton_poi_fused_cat_elu_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=[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_elu_23', '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_elu_23(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 512, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + ((256*x1) + ((-256) + x0)), tmp10, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + ((-256) + x0), tmp10, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + (x2), tmp25, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wn/cwnaxslf6yxgptz3rm7gmfd2bbkffrmpgjczpdgwqtpjubqilqbz.py
# Topologically Sorted Source Nodes: [out1_7, out2_7, out3, out4, out1_8, out2_8, out3_1, out4_1, add, add_1, out_14], Original ATen: [aten.convolution, aten.elu, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   out1_7 => convolution_22
#   out1_8 => expm1_15, gt_15, mul_45, mul_47, where_15
#   out2_7 => convolution_23
#   out2_8 => expm1_16, gt_16, mul_48, mul_50, where_16
#   out3 => convolution_24
#   out3_1 => expm1_17, gt_17, mul_51, mul_53, where_17
#   out4 => convolution_25
#   out4_1 => expm1_18, gt_18, mul_54, mul_56, where_18
#   out_14 => add_2
# Graph fragment:
#   %convolution_22 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_14, %primals_46, %primals_47, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_23 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_14, %primals_48, %primals_49, [1, 1], [2, 2], [2, 2], False, [0, 0], 1), kwargs = {})
#   %convolution_24 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_14, %primals_50, %primals_51, [1, 1], [3, 3], [3, 3], False, [0, 0], 1), kwargs = {})
#   %convolution_25 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_14, %primals_52, %primals_53, [1, 1], [4, 4], [4, 4], False, [0, 0], 1), kwargs = {})
#   %gt_15 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_22, 0), kwargs = {})
#   %mul_45 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_22, 1.0), kwargs = {})
#   %expm1_15 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_45,), kwargs = {})
#   %mul_47 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_15, 1.0), kwargs = {})
#   %where_15 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_15, %mul_45, %mul_47), kwargs = {})
#   %gt_16 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_23, 0), kwargs = {})
#   %mul_48 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_23, 1.0), kwargs = {})
#   %expm1_16 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_48,), kwargs = {})
#   %mul_50 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_16, 1.0), kwargs = {})
#   %where_16 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_16, %mul_48, %mul_50), kwargs = {})
#   %gt_17 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_24, 0), kwargs = {})
#   %mul_51 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_24, 1.0), kwargs = {})
#   %expm1_17 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_51,), kwargs = {})
#   %mul_53 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_17, 1.0), kwargs = {})
#   %where_17 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_17, %mul_51, %mul_53), kwargs = {})
#   %gt_18 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_25, 0), kwargs = {})
#   %mul_54 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_25, 1.0), kwargs = {})
#   %expm1_18 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_54,), kwargs = {})
#   %mul_56 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_18, 1.0), kwargs = {})
#   %where_18 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_18, %mul_54, %mul_56), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_15, %where_16), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %where_17), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %where_18), kwargs = {})
triton_poi_fused_add_convolution_elu_24 = async_compile.triton('triton_poi_fused_add_convolution_elu_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_elu_24', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2', 'in_out_ptr3'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_elu_24(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x2), None)
    tmp4 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr2 + (x2), None)
    tmp7 = tl.load(in_ptr2 + (x0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_out_ptr3 + (x2), None)
    tmp10 = tl.load(in_ptr3 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp2 > tmp12
    tmp14 = 1.0
    tmp15 = tmp2 * tmp14
    tmp16 = libdevice.expm1(tmp15)
    tmp17 = tmp16 * tmp14
    tmp18 = tl.where(tmp13, tmp15, tmp17)
    tmp19 = tmp5 > tmp12
    tmp20 = tmp5 * tmp14
    tmp21 = libdevice.expm1(tmp20)
    tmp22 = tmp21 * tmp14
    tmp23 = tl.where(tmp19, tmp20, tmp22)
    tmp24 = tmp18 + tmp23
    tmp25 = tmp8 > tmp12
    tmp26 = tmp8 * tmp14
    tmp27 = libdevice.expm1(tmp26)
    tmp28 = tmp27 * tmp14
    tmp29 = tl.where(tmp25, tmp26, tmp28)
    tmp30 = tmp24 + tmp29
    tmp31 = tmp11 > tmp12
    tmp32 = tmp11 * tmp14
    tmp33 = libdevice.expm1(tmp32)
    tmp34 = tmp33 * tmp14
    tmp35 = tl.where(tmp31, tmp32, tmp34)
    tmp36 = tmp30 + tmp35
    tl.store(in_out_ptr0 + (x2), tmp2, None)
    tl.store(in_out_ptr1 + (x2), tmp5, None)
    tl.store(in_out_ptr2 + (x2), tmp8, None)
    tl.store(in_out_ptr3 + (x2), tmp11, None)
    tl.store(out_ptr0 + (x2), tmp36, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qc/cqcdmfzsmd6ounnu6lacyvxezkzbeayqrlyqjqdalah6jwi7xokh.py
# Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   y_3 => convolution_26
# Graph fragment:
#   %convolution_26 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %primals_54, %primals_55, [2, 2], [1, 1], [1, 1], True, [1, 1], 1), kwargs = {})
triton_poi_fused_convolution_25 = async_compile.triton('triton_poi_fused_convolution_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_25', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_25(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mv/cmv65dt7ulmv3npo5tdighxcsvxsw6rg7ip5ezcpu7k7f47tyx4k.py
# Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_19 => convolution_27
#   x_20 => expm1_20, gt_20, mul_60, mul_62, where_20
# Graph fragment:
#   %convolution_27 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_8, %primals_56, %primals_57, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_20 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_27, 0), kwargs = {})
#   %mul_60 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_27, 1.0), kwargs = {})
#   %expm1_20 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_60,), kwargs = {})
#   %mul_62 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_20, 1.0), kwargs = {})
#   %where_20 : [num_users=5] = call_function[target=torch.ops.aten.where.self](args = (%gt_20, %mul_60, %mul_62), kwargs = {})
triton_poi_fused_convolution_elu_26 = async_compile.triton('triton_poi_fused_convolution_elu_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: '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_elu_26', '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_elu_26(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/p3/cp32qemuwpuw3fcp7rcbnl6ht5dvw7lvrm7lwf35oesnraxs5imm.py
# Topologically Sorted Source Nodes: [x_21], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x_21 => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
triton_poi_fused__to_copy_27 = async_compile.triton('triton_poi_fused__to_copy_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_27', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_27(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/as/casxgyqckqw2pc35egsdreksmtq4gyffwzuqhgvufqkcongvnz7u.py
# Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   x_21 => add_3, clamp_max
# Graph fragment:
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_1, 1), kwargs = {})
#   %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_3, 7), kwargs = {})
triton_poi_fused_add_clamp_28 = async_compile.triton('triton_poi_fused_add_clamp_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_28', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_28(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 7, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jb/cjb5b5jknub7ukvmbkmyp4d7lmimcyjhz45rskygdlm26l6owgmk.py
# Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   x_21 => clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul_63, sub
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
#   %mul_63 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 1.0), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_63, 0.0), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
triton_poi_fused__to_copy_arange_clamp_mul_sub_29 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_29', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_29(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = triton_helpers.minimum(tmp9, tmp2)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wl/cwl7ujdqxodhm4cybbu5vfew4gggkk6otbhacpwbapqfj2no3x4q.py
# Topologically Sorted Source Nodes: [x_21], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   x_21 => _unsafe_index, _unsafe_index_1, add_5, mul_65, sub_1
# Graph fragment:
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_20, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_20, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_65 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_65), kwargs = {})
triton_poi_fused__unsafe_index_add_mul_sub_30 = async_compile.triton('triton_poi_fused__unsafe_index_add_mul_sub_30', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*i64', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_mul_sub_30', '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__unsafe_index_add_mul_sub_30(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x2 = (xindex // 64) % 256
    x3 = (xindex // 16384)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (x2 + (256*tmp8) + (2048*tmp4) + (16384*x3)), None, eviction_policy='evict_last')
    tmp11 = tmp10 + tmp1
    tmp12 = tmp10 < 0
    tmp13 = tl.where(tmp12, tmp11, tmp10)
    tmp14 = tl.load(in_ptr2 + (x2 + (256*tmp13) + (2048*tmp4) + (16384*x3)), None, eviction_policy='evict_last')
    tmp15 = tmp14 - tmp9
    tmp17 = tmp15 * tmp16
    tmp18 = tmp9 + tmp17
    tl.store(out_ptr0 + (x5), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6n/c6nvb65oa2masaml7nrrj5axwi6oc4jljlrh2hz3onm6agnnhurj.py
# Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_22 => cat_7
# Graph fragment:
#   %cat_7 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%add_7, %where_19], 1), kwargs = {})
triton_poi_fused_cat_31 = async_compile.triton('triton_poi_fused_cat_31', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_31', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_31(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x3 = (xindex // 32768)
    x4 = (xindex // 512) % 64
    x2 = (xindex // 4096) % 8
    x1 = (xindex // 512) % 8
    x5 = (xindex // 512)
    x6 = 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 + (x4 + (64*x0) + (16384*x3)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 8, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr2 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr3 + ((256*tmp14) + (2048*tmp10) + (16384*x3) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr4 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp16 + tmp7
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr3 + ((256*tmp19) + (2048*tmp10) + (16384*x3) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp20 - tmp15
    tmp22 = tl.load(in_ptr5 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 * tmp22
    tmp24 = tmp15 + tmp23
    tmp25 = tmp24 - tmp5
    tmp26 = tl.load(in_ptr6 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp25 * tmp26
    tmp28 = tmp5 + tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp4, tmp28, tmp29)
    tmp31 = tmp0 >= tmp3
    tmp32 = tl.full([1], 512, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tl.load(in_ptr7 + ((256*x5) + ((-256) + x0)), tmp31, eviction_policy='evict_last', other=0.0)
    tmp35 = 0.0
    tmp36 = tmp34 > tmp35
    tmp37 = 1.0
    tmp38 = tmp34 * tmp37
    tmp39 = libdevice.expm1(tmp38)
    tmp40 = tmp39 * tmp37
    tmp41 = tl.where(tmp36, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp31, tmp41, tmp42)
    tmp44 = tl.where(tmp4, tmp30, tmp43)
    tl.store(out_ptr0 + (x6), tmp44, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rm/crmbhol5t242fv6f3xlidvzh4vl6ev2tff336rtenvoxp4htp4xn.py
# Topologically Sorted Source Nodes: [x_23, x_24], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_23 => convolution_28
#   x_24 => expm1_21, gt_21, mul_68, mul_70, where_21
# Graph fragment:
#   %convolution_28 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_7, %primals_58, %primals_59, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_21 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_28, 0), kwargs = {})
#   %mul_68 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_28, 1.0), kwargs = {})
#   %expm1_21 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_68,), kwargs = {})
#   %mul_70 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_21, 1.0), kwargs = {})
#   %where_21 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_21, %mul_68, %mul_70), kwargs = {})
triton_poi_fused_convolution_elu_32 = async_compile.triton('triton_poi_fused_convolution_elu_32', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_32', '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_elu_32(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7r/c7riwn5hoymrtgh2e5f7co7xofsidfabdnrxarp7nya2jdome47f.py
# Topologically Sorted Source Nodes: [y_2, y_5], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   y_2 => convolution_29
#   y_5 => expm1_22, gt_22, mul_71, mul_73, where_22
# Graph fragment:
#   %convolution_29 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_21, %primals_60, %primals_61, [2, 2], [1, 1], [1, 1], True, [1, 1], 1), kwargs = {})
#   %gt_22 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_29, 0), kwargs = {})
#   %mul_71 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_29, 1.0), kwargs = {})
#   %expm1_22 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_71,), kwargs = {})
#   %mul_73 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_22, 1.0), kwargs = {})
#   %where_22 : [num_users=5] = call_function[target=torch.ops.aten.where.self](args = (%gt_22, %mul_71, %mul_73), kwargs = {})
triton_poi_fused_convolution_elu_33 = async_compile.triton('triton_poi_fused_convolution_elu_33', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_33', '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_elu_33(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ef/cefflkzajjliw2qfqjnyhzv3l6q3zea4uvjcevhhl3mns6kqpyun.py
# Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_25 => convolution_30
# Graph fragment:
#   %convolution_30 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_62, %primals_63, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_34 = async_compile.triton('triton_poi_fused_convolution_34', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_34', '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_34(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/42/c42b3wsgtdm6q6ue4ray6j5wazcdarh2rcyfv6mb5jbyz2xodn5c.py
# Topologically Sorted Source Nodes: [y_6], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   y_6 => convert_element_type_5
# Graph fragment:
#   %convert_element_type_5 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_2, torch.int64), kwargs = {})
triton_poi_fused__to_copy_35 = async_compile.triton('triton_poi_fused__to_copy_35', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_35', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_35(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wi/cwibmh7qis7kvwfxabv3w57pwoaq3kn2uxxevznolth7xhvgbdio.py
# Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.add, aten.clamp]
# Source node to ATen node mapping:
#   y_6 => add_8, clamp_max_4
# Graph fragment:
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_5, 1), kwargs = {})
#   %clamp_max_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_8, 15), kwargs = {})
triton_poi_fused_add_clamp_36 = async_compile.triton('triton_poi_fused_add_clamp_36', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_36', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_36(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 15, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6i/c6i2i4ejbgi6u3djlreuj4ifcsdiesluilemg3ohpzp7hiqxqvhd.py
# Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
# Source node to ATen node mapping:
#   y_6 => clamp_max_6, clamp_min_4, clamp_min_6, convert_element_type_4, iota_2, mul_77, sub_5
# Graph fragment:
#   %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_2, torch.float32), kwargs = {})
#   %mul_77 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_4, 1.0), kwargs = {})
#   %clamp_min_4 : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_77, 0.0), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_4, %convert_element_type_7), kwargs = {})
#   %clamp_min_6 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_5, 0.0), kwargs = {})
#   %clamp_max_6 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_6, 1.0), kwargs = {})
triton_poi_fused__to_copy_arange_clamp_mul_sub_37 = async_compile.triton('triton_poi_fused__to_copy_arange_clamp_mul_sub_37', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_arange_clamp_mul_sub_37', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_37(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = triton_helpers.minimum(tmp9, tmp2)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4d/c4d65zf7gssvtswhtrr4iny4wxqc262jryl6zgjr4almabts2vye.py
# Topologically Sorted Source Nodes: [y_6], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   y_6 => _unsafe_index_4, _unsafe_index_5, add_10, mul_79, sub_6
# Graph fragment:
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_22, [None, None, %convert_element_type_5, %convert_element_type_7]), kwargs = {})
#   %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_22, [None, None, %convert_element_type_5, %clamp_max_5]), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_5, %_unsafe_index_4), kwargs = {})
#   %mul_79 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_6), kwargs = {})
#   %add_10 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_4, %mul_79), kwargs = {})
triton_poi_fused__unsafe_index_add_mul_sub_38 = async_compile.triton('triton_poi_fused__unsafe_index_add_mul_sub_38', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*i64', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_mul_sub_38', '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__unsafe_index_add_mul_sub_38(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x2 = (xindex // 256) % 128
    x3 = (xindex // 32768)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (x2 + (128*tmp8) + (2048*tmp4) + (32768*x3)), None, eviction_policy='evict_last')
    tmp11 = tmp10 + tmp1
    tmp12 = tmp10 < 0
    tmp13 = tl.where(tmp12, tmp11, tmp10)
    tmp14 = tl.load(in_ptr2 + (x2 + (128*tmp13) + (2048*tmp4) + (32768*x3)), None, eviction_policy='evict_last')
    tmp15 = tmp14 - tmp9
    tmp17 = tmp15 * tmp16
    tmp18 = tmp9 + tmp17
    tl.store(out_ptr0 + (x5), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nh/cnhnwdyfigrne3g7yt7smqvnrsy2r734te4chu4t2nvi4akuzbnq.py
# Topologically Sorted Source Nodes: [x_27], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_27 => cat_8
# Graph fragment:
#   %cat_8 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_23, %add_12], 1), kwargs = {})
triton_poi_fused_cat_39 = async_compile.triton('triton_poi_fused_cat_39', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 3: '*i64', 4: '*fp32', 5: '*i64', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_39', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_39(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x4 = (xindex // 256)
    x3 = (xindex // 65536)
    x5 = (xindex // 256) % 256
    x2 = (xindex // 4096) % 16
    x1 = (xindex // 256) % 16
    x6 = 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*x4) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 1.0
    tmp9 = tmp5 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 256, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr1 + (x5 + (256*((-128) + x0)) + (32768*x3)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.load(in_ptr2 + (x2), tmp15, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.full([XBLOCK], 16, tl.int32)
    tmp21 = tmp19 + tmp20
    tmp22 = tmp19 < 0
    tmp23 = tl.where(tmp22, tmp21, tmp19)
    tmp24 = tl.load(in_ptr3 + (x1), tmp15, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp20
    tmp26 = tmp24 < 0
    tmp27 = tl.where(tmp26, tmp25, tmp24)
    tmp28 = tl.load(in_ptr4 + ((128*tmp27) + (2048*tmp23) + (32768*x3) + ((-128) + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp29 = tl.load(in_ptr5 + (x1), tmp15, eviction_policy='evict_last', other=0.0)
    tmp30 = tmp29 + tmp20
    tmp31 = tmp29 < 0
    tmp32 = tl.where(tmp31, tmp30, tmp29)
    tmp33 = tl.load(in_ptr4 + ((128*tmp32) + (2048*tmp23) + (32768*x3) + ((-128) + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp34 = tmp33 - tmp28
    tmp35 = tl.load(in_ptr6 + (x1), tmp15, eviction_policy='evict_last', other=0.0)
    tmp36 = tmp34 * tmp35
    tmp37 = tmp28 + tmp36
    tmp38 = tmp37 - tmp18
    tmp39 = tl.load(in_ptr7 + (x2), tmp15, eviction_policy='evict_last', other=0.0)
    tmp40 = tmp38 * tmp39
    tmp41 = tmp18 + tmp40
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp15, tmp41, tmp42)
    tmp44 = tl.where(tmp4, tmp14, tmp43)
    tl.store(out_ptr0 + (x6), tmp44, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fy/cfy2ginubh5e5mm7llw72dgovlq4pvuc2xo4msrqs32xxfoaaqxy.py
# Topologically Sorted Source Nodes: [x_28, x_29], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_28 => convolution_31
#   x_29 => expm1_24, gt_24, mul_82, mul_84, where_24
# Graph fragment:
#   %convolution_31 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_8, %primals_64, %primals_65, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_24 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_31, 0), kwargs = {})
#   %mul_82 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_31, 1.0), kwargs = {})
#   %expm1_24 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_82,), kwargs = {})
#   %mul_84 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_24, 1.0), kwargs = {})
#   %where_24 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_24, %mul_82, %mul_84), kwargs = {})
triton_poi_fused_convolution_elu_40 = async_compile.triton('triton_poi_fused_convolution_elu_40', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_40', '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_elu_40(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ss/css3znsfuk5yfzm6tlm2gzva4ldrpatjhevx3a6bzuctkysm4zux.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   y_1 => convolution_32
# Graph fragment:
#   %convolution_32 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_24, %primals_66, %primals_67, [2, 2], [1, 1], [1, 1], True, [1, 1], 1), kwargs = {})
triton_poi_fused_convolution_41 = async_compile.triton('triton_poi_fused_convolution_41', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_41', '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_41(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 393216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 96
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6d/c6dykwn2jzvbptjkvlxz5htxp23myelffeadknq2ahb2kvhlkqxd.py
# Topologically Sorted Source Nodes: [x_32], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_32 => cat_9
# Graph fragment:
#   %cat_9 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%where_26, %where_25], 1), kwargs = {})
triton_poi_fused_cat_42 = async_compile.triton('triton_poi_fused_cat_42', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_42', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_42(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 786432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 192
    x1 = (xindex // 192)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 96, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((96*x1) + x0), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 1.0
    tmp9 = tmp5 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 192, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr1 + ((96*x1) + ((-96) + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 > tmp6
    tmp20 = tmp18 * tmp8
    tmp21 = libdevice.expm1(tmp20)
    tmp22 = tmp21 * tmp8
    tmp23 = tl.where(tmp19, tmp20, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp15, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp14, tmp25)
    tl.store(out_ptr0 + (x2), tmp26, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v7/cv7hzzgx3qudoscp2igix55axmqmmwspv2nmbekn2ofhvk5snecq.py
# Topologically Sorted Source Nodes: [x_33, x_34], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
#   x_33 => convolution_34
#   x_34 => expm1_27, gt_27, mul_91, mul_93, where_27
# Graph fragment:
#   %convolution_34 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_9, %primals_70, %primals_71, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_27 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_34, 0), kwargs = {})
#   %mul_91 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_34, 1.0), kwargs = {})
#   %expm1_27 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_91,), kwargs = {})
#   %mul_93 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_27, 1.0), kwargs = {})
#   %where_27 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_27, %mul_91, %mul_93), kwargs = {})
triton_poi_fused_convolution_elu_43 = async_compile.triton('triton_poi_fused_convolution_elu_43', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_43', '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_elu_43(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 786432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rb/crbrw6vlp54egqutuyaz42hhz3q34agdto7hq2ka7e6cxqy43cby.py
# Topologically Sorted Source Nodes: [x_35], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_35 => convolution_35
# Graph fragment:
#   %convolution_35 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_27, %primals_72, %primals_73, [2, 2], [1, 1], [1, 1], True, [1, 1], 1), kwargs = {})
triton_poi_fused_convolution_44 = async_compile.triton('triton_poi_fused_convolution_44', '''
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.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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_44', '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_44(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16384*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73 = args
    args.clear()
    assert_size_stride(primals_1, (96, 3, 3, 3), (27, 9, 3, 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, (64, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_29, (64, ), (1, ))
    assert_size_stride(primals_30, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_31, (256, ), (1, ))
    assert_size_stride(primals_32, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_33, (256, ), (1, ))
    assert_size_stride(primals_34, (64, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_35, (64, ), (1, ))
    assert_size_stride(primals_36, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_37, (256, ), (1, ))
    assert_size_stride(primals_38, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_39, (256, ), (1, ))
    assert_size_stride(primals_40, (64, 512, 1, 1), (512, 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, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_47, (512, ), (1, ))
    assert_size_stride(primals_48, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_49, (512, ), (1, ))
    assert_size_stride(primals_50, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_51, (512, ), (1, ))
    assert_size_stride(primals_52, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_53, (512, ), (1, ))
    assert_size_stride(primals_54, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_55, (256, ), (1, ))
    assert_size_stride(primals_56, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_57, (256, ), (1, ))
    assert_size_stride(primals_58, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_59, (512, ), (1, ))
    assert_size_stride(primals_60, (512, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_61, (128, ), (1, ))
    assert_size_stride(primals_62, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_63, (128, ), (1, ))
    assert_size_stride(primals_64, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_65, (256, ), (1, ))
    assert_size_stride(primals_66, (256, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_67, (96, ), (1, ))
    assert_size_stride(primals_68, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_69, (96, ), (1, ))
    assert_size_stride(primals_70, (192, 192, 3, 3), (1728, 9, 3, 1))
    assert_size_stride(primals_71, (192, ), (1, ))
    assert_size_stride(primals_72, (192, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_73, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 288, 9, grid=grid(288, 9), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 12, 4096, grid=grid(12, 4096), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((64, 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((256, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_32, buf6, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_32
        buf7 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_38, buf7, 16384, 9, grid=grid(16384, 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_4.run(primals_44, buf8, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_44
        buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_46, buf9, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_46
        buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_48, buf10, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_48
        buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_50, buf11, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_50
        buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_52, buf12, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_52
        buf13 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_54, buf13, 131072, 9, grid=grid(131072, 9), stream=stream0)
        del primals_54
        buf14 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_7.run(primals_56, buf14, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_56
        buf15 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_58, buf15, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_58
        buf16 = empty_strided_cuda((512, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_8.run(primals_60, buf16, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_60
        buf17 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(primals_62, buf17, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_62
        buf18 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_7.run(primals_64, buf18, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_64
        buf19 = empty_strided_cuda((256, 96, 3, 3), (864, 1, 288, 96), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_10.run(primals_66, buf19, 24576, 9, grid=grid(24576, 9), stream=stream0)
        del primals_66
        buf20 = empty_strided_cuda((96, 96, 3, 3), (864, 1, 288, 96), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_11.run(primals_68, buf20, 9216, 9, grid=grid(9216, 9), stream=stream0)
        del primals_68
        buf21 = empty_strided_cuda((192, 192, 3, 3), (1728, 1, 576, 192), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_12.run(primals_70, buf21, 36864, 9, grid=grid(36864, 9), stream=stream0)
        del primals_70
        buf22 = empty_strided_cuda((192, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_13.run(primals_72, buf22, 768, 9, grid=grid(768, 9), stream=stream0)
        del primals_72
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf23 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf24 = buf23; del buf23  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_14.run(buf24, primals_2, 393216, grid=grid(393216), stream=stream0)
        del primals_2
        buf25 = empty_strided_cuda((4, 96, 16, 16), (24576, 1, 1536, 96), torch.float32)
        buf26 = empty_strided_cuda((4, 96, 16, 16), (24576, 1, 1536, 96), torch.int8)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_15.run(buf24, buf25, buf26, 98304, grid=grid(98304), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf27 = extern_kernels.convolution(buf25, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 16, 16, 16), (4096, 1, 256, 16))
        buf28 = buf27; del buf27  # reuse
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_16.run(buf28, primals_5, 16384, grid=grid(16384), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [out1], Original ATen: [aten.convolution]
        buf29 = extern_kernels.convolution(buf28, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 64, 16, 16), (16384, 1, 1024, 64))
        # Topologically Sorted Source Nodes: [out2], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf28, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf31 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_17.run(buf29, primals_7, buf30, primals_9, buf31, 131072, grid=grid(131072), stream=stream0)
        del buf29
        del primals_7
        del primals_9
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 16, 16, 16), (4096, 1, 256, 16))
        buf33 = buf32; del buf32  # reuse
        # Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_16.run(buf33, primals_11, 16384, grid=grid(16384), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [out1_1], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf33, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 64, 16, 16), (16384, 1, 1024, 64))
        # Topologically Sorted Source Nodes: [out2_1], Original ATen: [aten.convolution]
        buf35 = extern_kernels.convolution(buf33, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf36 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.float32)
        # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_17.run(buf34, primals_13, buf35, primals_15, buf36, 131072, grid=grid(131072), stream=stream0)
        del primals_13
        del primals_15
        buf37 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128), torch.float32)
        buf38 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128), torch.int8)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_18.run(buf36, buf37, buf38, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf39 = extern_kernels.convolution(buf37, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 32, 8, 8), (2048, 1, 256, 32))
        buf40 = buf39; del buf39  # reuse
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_19.run(buf40, primals_17, 8192, grid=grid(8192), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [out1_2], Original ATen: [aten.convolution]
        buf41 = extern_kernels.convolution(buf40, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 128, 8, 8), (8192, 1, 1024, 128))
        # Topologically Sorted Source Nodes: [out2_2], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf40, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf43 = reinterpret_tensor(buf35, (4, 256, 8, 8), (16384, 1, 2048, 256), 0); del buf35  # reuse
        # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_20.run(buf41, primals_19, buf42, primals_21, buf43, 65536, grid=grid(65536), stream=stream0)
        del primals_19
        del primals_21
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf43, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 32, 8, 8), (2048, 1, 256, 32))
        buf45 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_19.run(buf45, primals_23, 8192, grid=grid(8192), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [out1_3], Original ATen: [aten.convolution]
        buf46 = extern_kernels.convolution(buf45, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 8, 8), (8192, 1, 1024, 128))
        # Topologically Sorted Source Nodes: [out2_3], Original ATen: [aten.convolution]
        buf47 = extern_kernels.convolution(buf45, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf48 = reinterpret_tensor(buf34, (4, 256, 8, 8), (16384, 1, 2048, 256), 0); del buf34  # reuse
        # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_20.run(buf46, primals_25, buf47, primals_27, buf48, 65536, grid=grid(65536), stream=stream0)
        del primals_25
        del primals_27
        buf49 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.float32)
        buf50 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.int8)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_21.run(buf48, buf49, buf50, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf49, primals_28, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf52 = buf51; del buf51  # reuse
        # Topologically Sorted Source Nodes: [x_13, x_14], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_22.run(buf52, primals_29, 4096, grid=grid(4096), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [out1_4], Original ATen: [aten.convolution]
        buf53 = extern_kernels.convolution(buf52, primals_30, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 256, 4, 4), (4096, 1, 1024, 256))
        # Topologically Sorted Source Nodes: [out2_4], Original ATen: [aten.convolution]
        buf54 = extern_kernels.convolution(buf52, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf55 = reinterpret_tensor(buf47, (4, 512, 4, 4), (8192, 1, 2048, 512), 0); del buf47  # reuse
        # Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_23.run(buf53, primals_31, buf54, primals_33, buf55, 32768, grid=grid(32768), stream=stream0)
        del buf53
        del buf54
        del primals_31
        del primals_33
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        buf56 = extern_kernels.convolution(buf55, primals_34, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf57 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [x_15, x_16], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_22.run(buf57, primals_35, 4096, grid=grid(4096), stream=stream0)
        del primals_35
        # Topologically Sorted Source Nodes: [out1_5], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, primals_36, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 256, 4, 4), (4096, 1, 1024, 256))
        # Topologically Sorted Source Nodes: [out2_5], Original ATen: [aten.convolution]
        buf59 = extern_kernels.convolution(buf57, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf60 = reinterpret_tensor(buf46, (4, 512, 4, 4), (8192, 1, 2048, 512), 0); del buf46  # reuse
        # Topologically Sorted Source Nodes: [out_10, out_11], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_23.run(buf58, primals_37, buf59, primals_39, buf60, 32768, grid=grid(32768), stream=stream0)
        del buf58
        del buf59
        del primals_37
        del primals_39
        # Topologically Sorted Source Nodes: [x_17], Original ATen: [aten.convolution]
        buf61 = extern_kernels.convolution(buf60, primals_40, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf61, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf62 = buf61; del buf61  # reuse
        # Topologically Sorted Source Nodes: [x_17, x_18], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_22.run(buf62, primals_41, 4096, grid=grid(4096), stream=stream0)
        del primals_41
        # Topologically Sorted Source Nodes: [out1_6], Original ATen: [aten.convolution]
        buf63 = extern_kernels.convolution(buf62, primals_42, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 256, 4, 4), (4096, 1, 1024, 256))
        # Topologically Sorted Source Nodes: [out2_6], Original ATen: [aten.convolution]
        buf64 = extern_kernels.convolution(buf62, buf8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf65 = reinterpret_tensor(buf42, (4, 512, 4, 4), (8192, 1, 2048, 512), 0); del buf42  # reuse
        # Topologically Sorted Source Nodes: [out_12, out_13], Original ATen: [aten.cat, aten.elu]
        triton_poi_fused_cat_elu_23.run(buf63, primals_43, buf64, primals_45, buf65, 32768, grid=grid(32768), stream=stream0)
        del buf63
        del buf64
        del primals_43
        del primals_45
        # Topologically Sorted Source Nodes: [out1_7], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, buf9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 512, 4, 4), (8192, 1, 2048, 512))
        # Topologically Sorted Source Nodes: [out2_7], Original ATen: [aten.convolution]
        buf68 = extern_kernels.convolution(buf65, buf10, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 512, 4, 4), (8192, 1, 2048, 512))
        # Topologically Sorted Source Nodes: [out3], Original ATen: [aten.convolution]
        buf70 = extern_kernels.convolution(buf65, buf11, stride=(1, 1), padding=(3, 3), dilation=(3, 3), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 512, 4, 4), (8192, 1, 2048, 512))
        # Topologically Sorted Source Nodes: [out4], Original ATen: [aten.convolution]
        buf72 = extern_kernels.convolution(buf65, buf12, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf72, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf67 = buf66; del buf66  # reuse
        buf69 = buf68; del buf68  # reuse
        buf71 = buf70; del buf70  # reuse
        buf73 = buf72; del buf72  # reuse
        buf74 = reinterpret_tensor(buf41, (4, 512, 4, 4), (8192, 1, 2048, 512), 0); del buf41  # reuse
        # Topologically Sorted Source Nodes: [out1_7, out2_7, out3, out4, out1_8, out2_8, out3_1, out4_1, add, add_1, out_14], Original ATen: [aten.convolution, aten.elu, aten.add]
        triton_poi_fused_add_convolution_elu_24.run(buf67, buf69, buf71, buf73, primals_47, primals_49, primals_51, primals_53, buf74, 32768, grid=grid(32768), stream=stream0)
        del primals_47
        del primals_49
        del primals_51
        del primals_53
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.convolution]
        buf75 = extern_kernels.convolution(buf74, buf13, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf75, (4, 256, 8, 8), (16384, 1, 2048, 256))
        buf76 = buf75; del buf75  # reuse
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_25.run(buf76, primals_55, 65536, grid=grid(65536), stream=stream0)
        del primals_55
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.convolution]
        buf77 = extern_kernels.convolution(buf48, buf14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf77, (4, 256, 8, 8), (16384, 1, 2048, 256))
        buf78 = buf77; del buf77  # reuse
        # Topologically Sorted Source Nodes: [x_19, x_20], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_26.run(buf78, primals_57, 65536, grid=grid(65536), stream=stream0)
        del primals_57
        buf79 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_27.run(buf79, 8, grid=grid(8), stream=stream0)
        buf80 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_28.run(buf80, 8, grid=grid(8), stream=stream0)
        buf81 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_27.run(buf81, 8, grid=grid(8), stream=stream0)
        buf82 = empty_strided_cuda((8, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_28.run(buf82, 8, grid=grid(8), stream=stream0)
        buf83 = empty_strided_cuda((8, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_29.run(buf83, 8, grid=grid(8), stream=stream0)
        buf84 = reinterpret_tensor(buf30, (4, 256, 8, 8), (16384, 64, 8, 1), 0); del buf30  # reuse
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_mul_sub_30.run(buf79, buf81, buf78, buf82, buf83, buf84, 65536, grid=grid(65536), stream=stream0)
        buf85 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_29.run(buf85, 8, grid=grid(8), stream=stream0)
        buf86 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.float32)
        # Topologically Sorted Source Nodes: [x_22], Original ATen: [aten.cat]
        triton_poi_fused_cat_31.run(buf84, buf80, buf81, buf78, buf82, buf83, buf85, buf76, buf86, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [x_23], Original ATen: [aten.convolution]
        buf87 = extern_kernels.convolution(buf86, buf15, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf88 = buf87; del buf87  # reuse
        # Topologically Sorted Source Nodes: [x_23, x_24], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_32.run(buf88, primals_59, 131072, grid=grid(131072), stream=stream0)
        del primals_59
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.convolution]
        buf89 = extern_kernels.convolution(buf88, buf16, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf89, (4, 128, 16, 16), (32768, 1, 2048, 128))
        buf90 = buf89; del buf89  # reuse
        # Topologically Sorted Source Nodes: [y_2, y_5], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_33.run(buf90, primals_61, 131072, grid=grid(131072), stream=stream0)
        del primals_61
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.convolution]
        buf91 = extern_kernels.convolution(buf36, buf17, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf91, (4, 128, 16, 16), (32768, 1, 2048, 128))
        buf92 = buf91; del buf91  # reuse
        # Topologically Sorted Source Nodes: [x_25], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_34.run(buf92, primals_63, 131072, grid=grid(131072), stream=stream0)
        del primals_63
        buf93 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_35.run(buf93, 16, grid=grid(16), stream=stream0)
        buf94 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_36.run(buf94, 16, grid=grid(16), stream=stream0)
        buf95 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp]
        triton_poi_fused__to_copy_35.run(buf95, 16, grid=grid(16), stream=stream0)
        buf96 = empty_strided_cuda((16, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.add, aten.clamp]
        triton_poi_fused_add_clamp_36.run(buf96, 16, grid=grid(16), stream=stream0)
        buf97 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten.sub]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_37.run(buf97, 16, grid=grid(16), stream=stream0)
        buf98 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add]
        triton_poi_fused__unsafe_index_add_mul_sub_38.run(buf93, buf95, buf90, buf96, buf97, buf98, 131072, grid=grid(131072), stream=stream0)
        buf99 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.sub, aten.clamp]
        triton_poi_fused__to_copy_arange_clamp_mul_sub_37.run(buf99, 16, grid=grid(16), stream=stream0)
        buf100 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.float32)
        # Topologically Sorted Source Nodes: [x_27], Original ATen: [aten.cat]
        triton_poi_fused_cat_39.run(buf92, buf98, buf94, buf95, buf90, buf96, buf97, buf99, buf100, 262144, grid=grid(262144), stream=stream0)
        del buf98
        # Topologically Sorted Source Nodes: [x_28], Original ATen: [aten.convolution]
        buf101 = extern_kernels.convolution(buf100, buf18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf102 = buf101; del buf101  # reuse
        # Topologically Sorted Source Nodes: [x_28, x_29], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_40.run(buf102, primals_65, 262144, grid=grid(262144), stream=stream0)
        del primals_65
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
        buf103 = extern_kernels.convolution(buf102, buf19, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf103, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf104 = buf103; del buf103  # reuse
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_41.run(buf104, primals_67, 393216, grid=grid(393216), stream=stream0)
        del primals_67
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.convolution]
        buf105 = extern_kernels.convolution(buf24, buf20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf105, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf106 = buf105; del buf105  # reuse
        # Topologically Sorted Source Nodes: [x_30], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_41.run(buf106, primals_69, 393216, grid=grid(393216), stream=stream0)
        del primals_69
        buf107 = empty_strided_cuda((4, 192, 32, 32), (196608, 1, 6144, 192), torch.float32)
        # Topologically Sorted Source Nodes: [x_32], Original ATen: [aten.cat]
        triton_poi_fused_cat_42.run(buf106, buf104, buf107, 786432, grid=grid(786432), stream=stream0)
        # Topologically Sorted Source Nodes: [x_33], Original ATen: [aten.convolution]
        buf108 = extern_kernels.convolution(buf107, buf21, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 192, 32, 32), (196608, 1, 6144, 192))
        buf109 = buf108; del buf108  # reuse
        # Topologically Sorted Source Nodes: [x_33, x_34], Original ATen: [aten.convolution, aten.elu]
        triton_poi_fused_convolution_elu_43.run(buf109, primals_71, 786432, grid=grid(786432), stream=stream0)
        del primals_71
        # Topologically Sorted Source Nodes: [x_35], Original ATen: [aten.convolution]
        buf110 = extern_kernels.convolution(buf109, buf22, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf110, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf111 = reinterpret_tensor(buf84, (4, 4, 64, 64), (16384, 4096, 64, 1), 0); del buf84  # reuse
        # Topologically Sorted Source Nodes: [x_35], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_44.run(buf110, primals_73, buf111, 16, 4096, grid=grid(16, 4096), stream=stream0)
        del buf110
        del primals_73
    return (buf111, 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, buf9, buf10, buf11, buf12, buf13, buf14, buf15, buf16, buf17, buf18, buf19, buf20, buf21, buf22, buf24, buf25, buf26, buf28, buf31, buf33, buf36, buf37, buf38, buf40, buf43, buf45, buf48, buf49, buf50, buf52, buf55, buf57, buf60, buf62, buf65, buf67, buf69, buf71, buf73, buf74, buf76, buf78, buf79, buf80, buf81, buf82, buf83, buf85, buf86, buf88, buf90, buf92, buf93, buf94, buf95, buf96, buf97, buf99, buf100, buf102, buf104, buf106, buf107, buf109, )


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, 3, 3), (27, 9, 3, 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((64, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((256, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_34 = rand_strided((64, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_35 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_36 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_37 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_38 = rand_strided((256, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_39 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_40 = rand_strided((64, 512, 1, 1), (512, 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((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_47 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_48 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_49 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_50 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_51 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_52 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_53 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_54 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_55 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_56 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_57 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_58 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_59 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_60 = rand_strided((512, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_61 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_62 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_63 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_64 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_65 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_66 = rand_strided((256, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_67 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_68 = rand_strided((96, 96, 3, 3), (864, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_69 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_70 = rand_strided((192, 192, 3, 3), (1728, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_71 = rand_strided((192, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_72 = rand_strided((192, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_73 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33, primals_34, primals_35, primals_36, primals_37, primals_38, primals_39, primals_40, primals_41, primals_42, primals_43, primals_44, primals_45, primals_46, primals_47, primals_48, primals_49, primals_50, primals_51, primals_52, primals_53, primals_54, primals_55, primals_56, primals_57, primals_58, primals_59, primals_60, primals_61, primals_62, primals_63, primals_64, primals_65, primals_66, primals_67, primals_68, primals_69, primals_70, primals_71, primals_72, primals_73])
    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 math
import torch
import torch.nn as nn
import torch.nn.functional as F


class Fire(nn.Module):

    def __init__(self, inplanes, squeeze_planes, expand_planes):
        super(Fire, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1,
            stride=1)
        self.relu1 = nn.ELU(inplace=True)
        self.conv2 = nn.Conv2d(squeeze_planes, expand_planes, kernel_size=1,
            stride=1)
        self.conv3 = nn.Conv2d(squeeze_planes, expand_planes, kernel_size=3,
            stride=1, padding=1)
        self.relu2 = nn.ELU(inplace=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        out1 = self.conv2(x)
        out2 = self.conv3(x)
        out = torch.cat([out1, out2], 1)
        out = self.relu2(out)
        return out


class ParallelDilatedConv(nn.Module):

    def __init__(self, inplanes, planes):
        super(ParallelDilatedConv, self).__init__()
        self.dilated_conv_1 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=1, dilation=1)
        self.dilated_conv_2 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=2, dilation=2)
        self.dilated_conv_3 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=3, dilation=3)
        self.dilated_conv_4 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=4, dilation=4)
        self.relu1 = nn.ELU(inplace=True)
        self.relu2 = nn.ELU(inplace=True)
        self.relu3 = nn.ELU(inplace=True)
        self.relu4 = nn.ELU(inplace=True)

    def forward(self, x):
        out1 = self.dilated_conv_1(x)
        out2 = self.dilated_conv_2(x)
        out3 = self.dilated_conv_3(x)
        out4 = self.dilated_conv_4(x)
        out1 = self.relu1(out1)
        out2 = self.relu2(out2)
        out3 = self.relu3(out3)
        out4 = self.relu4(out4)
        out = out1 + out2 + out3 + out4
        return out


class SQNet(nn.Module):

    def __init__(self, classes):
        super().__init__()
        self.num_classes = classes
        self.conv1 = nn.Conv2d(3, 96, kernel_size=3, stride=2, padding=1)
        self.relu1 = nn.ELU(inplace=True)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire1_1 = Fire(96, 16, 64)
        self.fire1_2 = Fire(128, 16, 64)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire2_1 = Fire(128, 32, 128)
        self.fire2_2 = Fire(256, 32, 128)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire3_1 = Fire(256, 64, 256)
        self.fire3_2 = Fire(512, 64, 256)
        self.fire3_3 = Fire(512, 64, 256)
        self.parallel = ParallelDilatedConv(512, 512)
        self.deconv1 = nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1,
            output_padding=1)
        self.relu2 = nn.ELU(inplace=True)
        self.deconv2 = nn.ConvTranspose2d(512, 128, 3, stride=2, padding=1,
            output_padding=1)
        self.relu3 = nn.ELU(inplace=True)
        self.deconv3 = nn.ConvTranspose2d(256, 96, 3, stride=2, padding=1,
            output_padding=1)
        self.relu4 = nn.ELU(inplace=True)
        self.deconv4 = nn.ConvTranspose2d(192, self.num_classes, 3, stride=
            2, padding=1, output_padding=1)
        self.conv3_1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv2_1 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.conv2_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv1_1 = nn.Conv2d(96, 96, kernel_size=3, stride=1, padding=1)
        self.conv1_2 = nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1)
        self.relu1_1 = nn.ELU(inplace=True)
        self.relu1_2 = nn.ELU(inplace=True)
        self.relu2_1 = nn.ELU(inplace=True)
        self.relu2_2 = nn.ELU(inplace=True)
        self.relu3_1 = nn.ELU(inplace=True)
        self.relu3_2 = nn.ELU(inplace=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, x):
        x = self.conv1(x)
        x_1 = self.relu1(x)
        x = self.maxpool1(x_1)
        x = self.fire1_1(x)
        x_2 = self.fire1_2(x)
        x = self.maxpool2(x_2)
        x = self.fire2_1(x)
        x_3 = self.fire2_2(x)
        x = self.maxpool3(x_3)
        x = self.fire3_1(x)
        x = self.fire3_2(x)
        x = self.fire3_3(x)
        x = self.parallel(x)
        y_3 = self.deconv1(x)
        y_3 = self.relu2(y_3)
        x_3 = self.conv3_1(x_3)
        x_3 = self.relu3_1(x_3)
        x_3 = F.interpolate(x_3, y_3.size()[2:], mode='bilinear',
            align_corners=True)
        x = torch.cat([x_3, y_3], 1)
        x = self.conv3_2(x)
        x = self.relu3_2(x)
        y_2 = self.deconv2(x)
        y_2 = self.relu3(y_2)
        x_2 = self.conv2_1(x_2)
        x_2 = self.relu2_1(x_2)
        y_2 = F.interpolate(y_2, x_2.size()[2:], mode='bilinear',
            align_corners=True)
        x = torch.cat([x_2, y_2], 1)
        x = self.conv2_2(x)
        x = self.relu2_2(x)
        y_1 = self.deconv3(x)
        y_1 = self.relu4(y_1)
        x_1 = self.conv1_1(x_1)
        x_1 = self.relu1_1(x_1)
        x = torch.cat([x_1, y_1], 1)
        x = self.conv1_2(x)
        x = self.relu1_2(x)
        x = self.deconv4(x)
        return x


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


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

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


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


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 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 % 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_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


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


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


@triton.jit
def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_10(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 % 96
    y1 = yindex // 96
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 96 * x2 + 864 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_11(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 % 96
    y1 = yindex // 96
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 96 * x2 + 864 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_12(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 % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 192 * x2 + 1728 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_13(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 768
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_elu_14(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 96
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_15(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 % 96
    x1 = xindex // 96 % 16
    x2 = xindex // 1536
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 192 * x1 + 6144 * x2), None)
    tmp1 = tl.load(in_ptr0 + (96 + x0 + 192 * x1 + 6144 * x2), None)
    tmp3 = tl.load(in_ptr0 + (3072 + x0 + 192 * x1 + 6144 * x2), None)
    tmp5 = tl.load(in_ptr0 + (3168 + x0 + 192 * x1 + 6144 * x2), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_elu_16(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_cat_elu_17(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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp13 = tl.load(in_ptr2 + (64 * x1 + (-64 + x0)), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + (-64 + x0), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + x2, tmp25, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_18(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 % 128
    x1 = xindex // 128 % 8
    x2 = xindex // 1024
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 4096 * x2), None)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 4096 * x2), None)
    tmp3 = tl.load(in_ptr0 + (2048 + x0 + 256 * x1 + 4096 * x2), None)
    tmp5 = tl.load(in_ptr0 + (2176 + x0 + 256 * x1 + 4096 * x2), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_elu_19(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_cat_elu_20(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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp13 = tl.load(in_ptr2 + (128 * x1 + (-128 + x0)), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + (-128 + x0), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + x2, tmp25, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_21(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 % 256
    x1 = xindex // 256 % 4
    x2 = xindex // 1024
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 4096 * x2), None)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 4096 * x2), None)
    tmp3 = tl.load(in_ptr0 + (2048 + x0 + 512 * x1 + 4096 * x2), None)
    tmp5 = tl.load(in_ptr0 + (2304 + x0 + 512 * x1 + 4096 * x2), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, None)
    tl.store(out_ptr1 + x3, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_elu_22(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_cat_elu_23(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(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp13 = tl.load(in_ptr2 + (256 * x1 + (-256 + x0)), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr3 + (-256 + x0), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp19 = 0.0
    tmp20 = tmp18 > tmp19
    tmp21 = 1.0
    tmp22 = tmp18 * tmp21
    tmp23 = libdevice.expm1(tmp22)
    tmp24 = tmp23 * tmp21
    tmp25 = tl.where(tmp20, tmp22, tmp24)
    tl.store(out_ptr0 + x2, tmp25, None)


@triton.jit
def triton_poi_fused_add_convolution_elu_24(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_out_ptr3, 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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, None)
    tmp4 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr2 + x2, None)
    tmp7 = tl.load(in_ptr2 + x0, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_out_ptr3 + x2, None)
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp2 > tmp12
    tmp14 = 1.0
    tmp15 = tmp2 * tmp14
    tmp16 = libdevice.expm1(tmp15)
    tmp17 = tmp16 * tmp14
    tmp18 = tl.where(tmp13, tmp15, tmp17)
    tmp19 = tmp5 > tmp12
    tmp20 = tmp5 * tmp14
    tmp21 = libdevice.expm1(tmp20)
    tmp22 = tmp21 * tmp14
    tmp23 = tl.where(tmp19, tmp20, tmp22)
    tmp24 = tmp18 + tmp23
    tmp25 = tmp8 > tmp12
    tmp26 = tmp8 * tmp14
    tmp27 = libdevice.expm1(tmp26)
    tmp28 = tmp27 * tmp14
    tmp29 = tl.where(tmp25, tmp26, tmp28)
    tmp30 = tmp24 + tmp29
    tmp31 = tmp11 > tmp12
    tmp32 = tmp11 * tmp14
    tmp33 = libdevice.expm1(tmp32)
    tmp34 = tmp33 * tmp14
    tmp35 = tl.where(tmp31, tmp32, tmp34)
    tmp36 = tmp30 + tmp35
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(in_out_ptr1 + x2, tmp5, None)
    tl.store(in_out_ptr2 + x2, tmp8, None)
    tl.store(in_out_ptr3 + x2, tmp11, None)
    tl.store(out_ptr0 + x2, tmp36, None)


@triton.jit
def triton_poi_fused_convolution_25(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_elu_26(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused__to_copy_27(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_28(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 7, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_29(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = triton_helpers.minimum(tmp9, tmp2)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_mul_sub_30(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64 % 256
    x3 = xindex // 16384
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 8, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (x2 + 256 * tmp8 + 2048 * tmp4 + 16384 * x3),
        None, eviction_policy='evict_last')
    tmp11 = tmp10 + tmp1
    tmp12 = tmp10 < 0
    tmp13 = tl.where(tmp12, tmp11, tmp10)
    tmp14 = tl.load(in_ptr2 + (x2 + 256 * tmp13 + 2048 * tmp4 + 16384 * x3),
        None, eviction_policy='evict_last')
    tmp15 = tmp14 - tmp9
    tmp17 = tmp15 * tmp16
    tmp18 = tmp9 + tmp17
    tl.store(out_ptr0 + x5, tmp18, None)


@triton.jit
def triton_poi_fused_cat_31(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, 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
    x3 = xindex // 32768
    x4 = xindex // 512 % 64
    x2 = xindex // 4096 % 8
    x1 = xindex // 512 % 8
    x5 = xindex // 512
    x6 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 256, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 64 * x0 + 16384 * x3), tmp4,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.full([XBLOCK], 8, tl.int32)
    tmp8 = tmp6 + tmp7
    tmp9 = tmp6 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp6)
    tmp11 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0
        )
    tmp12 = tmp11 + tmp7
    tmp13 = tmp11 < 0
    tmp14 = tl.where(tmp13, tmp12, tmp11)
    tmp15 = tl.load(in_ptr3 + (256 * tmp14 + 2048 * tmp10 + 16384 * x3 + x0
        ), tmp4, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr4 + x1, tmp4, eviction_policy='evict_last', other=0.0
        )
    tmp17 = tmp16 + tmp7
    tmp18 = tmp16 < 0
    tmp19 = tl.where(tmp18, tmp17, tmp16)
    tmp20 = tl.load(in_ptr3 + (256 * tmp19 + 2048 * tmp10 + 16384 * x3 + x0
        ), tmp4, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp20 - tmp15
    tmp22 = tl.load(in_ptr5 + x1, tmp4, eviction_policy='evict_last', other=0.0
        )
    tmp23 = tmp21 * tmp22
    tmp24 = tmp15 + tmp23
    tmp25 = tmp24 - tmp5
    tmp26 = tl.load(in_ptr6 + x2, tmp4, eviction_policy='evict_last', other=0.0
        )
    tmp27 = tmp25 * tmp26
    tmp28 = tmp5 + tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp4, tmp28, tmp29)
    tmp31 = tmp0 >= tmp3
    tl.full([1], 512, tl.int64)
    tmp34 = tl.load(in_ptr7 + (256 * x5 + (-256 + x0)), tmp31,
        eviction_policy='evict_last', other=0.0)
    tmp35 = 0.0
    tmp36 = tmp34 > tmp35
    tmp37 = 1.0
    tmp38 = tmp34 * tmp37
    tmp39 = libdevice.expm1(tmp38)
    tmp40 = tmp39 * tmp37
    tmp41 = tl.where(tmp36, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp31, tmp41, tmp42)
    tmp44 = tl.where(tmp4, tmp30, tmp43)
    tl.store(out_ptr0 + x6, tmp44, None)


@triton.jit
def triton_poi_fused_convolution_elu_32(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_elu_33(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_34(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused__to_copy_35(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_clamp_36(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 15, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_37(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 - tmp7
    tmp9 = triton_helpers.maximum(tmp8, tmp4)
    tmp10 = triton_helpers.minimum(tmp9, tmp2)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_mul_sub_38(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256 % 128
    x3 = xindex // 32768
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (x2 + 128 * tmp8 + 2048 * tmp4 + 32768 * x3),
        None, eviction_policy='evict_last')
    tmp11 = tmp10 + tmp1
    tmp12 = tmp10 < 0
    tmp13 = tl.where(tmp12, tmp11, tmp10)
    tmp14 = tl.load(in_ptr2 + (x2 + 128 * tmp13 + 2048 * tmp4 + 32768 * x3),
        None, eviction_policy='evict_last')
    tmp15 = tmp14 - tmp9
    tmp17 = tmp15 * tmp16
    tmp18 = tmp9 + tmp17
    tl.store(out_ptr0 + x5, tmp18, None)


@triton.jit
def triton_poi_fused_cat_39(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, 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
    x4 = xindex // 256
    x3 = xindex // 65536
    x5 = xindex // 256 % 256
    x2 = xindex // 4096 % 16
    x1 = xindex // 256 % 16
    x6 = 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 * x4 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 1.0
    tmp9 = tmp5 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp18 = tl.load(in_ptr1 + (x5 + 256 * (-128 + x0) + 32768 * x3), tmp15,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tl.load(in_ptr2 + x2, tmp15, eviction_policy='evict_last',
        other=0.0)
    tmp20 = tl.full([XBLOCK], 16, tl.int32)
    tmp21 = tmp19 + tmp20
    tmp22 = tmp19 < 0
    tmp23 = tl.where(tmp22, tmp21, tmp19)
    tmp24 = tl.load(in_ptr3 + x1, tmp15, eviction_policy='evict_last',
        other=0.0)
    tmp25 = tmp24 + tmp20
    tmp26 = tmp24 < 0
    tmp27 = tl.where(tmp26, tmp25, tmp24)
    tmp28 = tl.load(in_ptr4 + (128 * tmp27 + 2048 * tmp23 + 32768 * x3 + (-
        128 + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp29 = tl.load(in_ptr5 + x1, tmp15, eviction_policy='evict_last',
        other=0.0)
    tmp30 = tmp29 + tmp20
    tmp31 = tmp29 < 0
    tmp32 = tl.where(tmp31, tmp30, tmp29)
    tmp33 = tl.load(in_ptr4 + (128 * tmp32 + 2048 * tmp23 + 32768 * x3 + (-
        128 + x0)), tmp15, eviction_policy='evict_last', other=0.0)
    tmp34 = tmp33 - tmp28
    tmp35 = tl.load(in_ptr6 + x1, tmp15, eviction_policy='evict_last',
        other=0.0)
    tmp36 = tmp34 * tmp35
    tmp37 = tmp28 + tmp36
    tmp38 = tmp37 - tmp18
    tmp39 = tl.load(in_ptr7 + x2, tmp15, eviction_policy='evict_last',
        other=0.0)
    tmp40 = tmp38 * tmp39
    tmp41 = tmp18 + tmp40
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp15, tmp41, tmp42)
    tmp44 = tl.where(tmp4, tmp14, tmp43)
    tl.store(out_ptr0 + x6, tmp44, None)


@triton.jit
def triton_poi_fused_convolution_elu_40(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_41(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 96
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_cat_42(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 192
    x1 = xindex // 192
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 96, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (96 * x1 + x0), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 1.0
    tmp9 = tmp5 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tl.full([1], 192, tl.int64)
    tmp18 = tl.load(in_ptr1 + (96 * x1 + (-96 + x0)), tmp15,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 > tmp6
    tmp20 = tmp18 * tmp8
    tmp21 = libdevice.expm1(tmp20)
    tmp22 = tmp21 * tmp8
    tmp23 = tl.where(tmp19, tmp20, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp15, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp14, tmp25)
    tl.store(out_ptr0 + x2, tmp26, None)


@triton.jit
def triton_poi_fused_convolution_elu_43(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_44(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59, primals_60, primals_61, primals_62,
        primals_63, primals_64, primals_65, primals_66, primals_67,
        primals_68, primals_69, primals_70, primals_71, primals_72, primals_73
        ) = args
    args.clear()
    assert_size_stride(primals_1, (96, 3, 3, 3), (27, 9, 3, 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, (64, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_29, (64,), (1,))
    assert_size_stride(primals_30, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_31, (256,), (1,))
    assert_size_stride(primals_32, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_33, (256,), (1,))
    assert_size_stride(primals_34, (64, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_35, (64,), (1,))
    assert_size_stride(primals_36, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_37, (256,), (1,))
    assert_size_stride(primals_38, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_39, (256,), (1,))
    assert_size_stride(primals_40, (64, 512, 1, 1), (512, 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, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_47, (512,), (1,))
    assert_size_stride(primals_48, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_49, (512,), (1,))
    assert_size_stride(primals_50, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_51, (512,), (1,))
    assert_size_stride(primals_52, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_53, (512,), (1,))
    assert_size_stride(primals_54, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_55, (256,), (1,))
    assert_size_stride(primals_56, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_57, (256,), (1,))
    assert_size_stride(primals_58, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_59, (512,), (1,))
    assert_size_stride(primals_60, (512, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_61, (128,), (1,))
    assert_size_stride(primals_62, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_63, (128,), (1,))
    assert_size_stride(primals_64, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_65, (256,), (1,))
    assert_size_stride(primals_66, (256, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_67, (96,), (1,))
    assert_size_stride(primals_68, (96, 96, 3, 3), (864, 9, 3, 1))
    assert_size_stride(primals_69, (96,), (1,))
    assert_size_stride(primals_70, (192, 192, 3, 3), (1728, 9, 3, 1))
    assert_size_stride(primals_71, (192,), (1,))
    assert_size_stride(primals_72, (192, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_73, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(288, 9)](primals_1, buf0, 288, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 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((256, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_4[grid(16384, 9)](primals_32, buf6, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_32
        buf7 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_4[grid(16384, 9)](primals_38, buf7, 16384, 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_4[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((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_5[grid(262144, 9)](primals_46, buf9, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_46
        buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_5[grid(262144, 9)](primals_48, buf10, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_48
        buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_5[grid(262144, 9)](primals_50, buf11, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_50
        buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_5[grid(262144, 9)](primals_52, buf12, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_52
        buf13 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_6[grid(131072, 9)](primals_54, buf13, 131072, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_54
        buf14 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_7[grid(65536, 9)](primals_56, buf14, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_56
        buf15 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_5[grid(262144, 9)](primals_58, buf15, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_58
        buf16 = empty_strided_cuda((512, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_8[grid(65536, 9)](primals_60, buf16, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_60
        buf17 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_9[grid(16384, 9)](primals_62, buf17, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_62
        buf18 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_7[grid(65536, 9)](primals_64, buf18, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_64
        buf19 = empty_strided_cuda((256, 96, 3, 3), (864, 1, 288, 96),
            torch.float32)
        triton_poi_fused_10[grid(24576, 9)](primals_66, buf19, 24576, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_66
        buf20 = empty_strided_cuda((96, 96, 3, 3), (864, 1, 288, 96), torch
            .float32)
        triton_poi_fused_11[grid(9216, 9)](primals_68, buf20, 9216, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_68
        buf21 = empty_strided_cuda((192, 192, 3, 3), (1728, 1, 576, 192),
            torch.float32)
        triton_poi_fused_12[grid(36864, 9)](primals_70, buf21, 36864, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_70
        buf22 = empty_strided_cuda((192, 4, 3, 3), (36, 1, 12, 4), torch.
            float32)
        triton_poi_fused_13[grid(768, 9)](primals_72, buf22, 768, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_72
        buf23 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf24 = buf23
        del buf23
        triton_poi_fused_convolution_elu_14[grid(393216)](buf24, primals_2,
            393216, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf25 = empty_strided_cuda((4, 96, 16, 16), (24576, 1, 1536, 96),
            torch.float32)
        buf26 = empty_strided_cuda((4, 96, 16, 16), (24576, 1, 1536, 96),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_15[grid(98304)](buf24,
            buf25, buf26, 98304, XBLOCK=512, num_warps=8, num_stages=1)
        buf27 = extern_kernels.convolution(buf25, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf27, (4, 16, 16, 16), (4096, 1, 256, 16))
        buf28 = buf27
        del buf27
        triton_poi_fused_convolution_elu_16[grid(16384)](buf28, primals_5, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf29 = extern_kernels.convolution(buf28, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf30 = extern_kernels.convolution(buf28, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf31 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
            torch.float32)
        triton_poi_fused_cat_elu_17[grid(131072)](buf29, primals_7, buf30,
            primals_9, buf31, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del buf29
        del primals_7
        del primals_9
        buf32 = extern_kernels.convolution(buf31, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 16, 16, 16), (4096, 1, 256, 16))
        buf33 = buf32
        del buf32
        triton_poi_fused_convolution_elu_16[grid(16384)](buf33, primals_11,
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf34 = extern_kernels.convolution(buf33, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf35 = extern_kernels.convolution(buf33, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf35, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf36 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
            torch.float32)
        triton_poi_fused_cat_elu_17[grid(131072)](buf34, primals_13, buf35,
            primals_15, buf36, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_13
        del primals_15
        buf37 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128),
            torch.float32)
        buf38 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_18[grid(32768)](buf36,
            buf37, buf38, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        buf39 = extern_kernels.convolution(buf37, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf39, (4, 32, 8, 8), (2048, 1, 256, 32))
        buf40 = buf39
        del buf39
        triton_poi_fused_convolution_elu_19[grid(8192)](buf40, primals_17, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf41 = extern_kernels.convolution(buf40, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf41, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf42 = extern_kernels.convolution(buf40, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf43 = reinterpret_tensor(buf35, (4, 256, 8, 8), (16384, 1, 2048, 
            256), 0)
        del buf35
        triton_poi_fused_cat_elu_20[grid(65536)](buf41, primals_19, buf42,
            primals_21, buf43, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_19
        del primals_21
        buf44 = extern_kernels.convolution(buf43, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 32, 8, 8), (2048, 1, 256, 32))
        buf45 = buf44
        del buf44
        triton_poi_fused_convolution_elu_19[grid(8192)](buf45, primals_23, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_23
        buf46 = extern_kernels.convolution(buf45, primals_24, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf47 = extern_kernels.convolution(buf45, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf48 = reinterpret_tensor(buf34, (4, 256, 8, 8), (16384, 1, 2048, 
            256), 0)
        del buf34
        triton_poi_fused_cat_elu_20[grid(65536)](buf46, primals_25, buf47,
            primals_27, buf48, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_25
        del primals_27
        buf49 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
            torch.float32)
        buf50 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_21[grid(16384)](buf48,
            buf49, buf50, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        buf51 = extern_kernels.convolution(buf49, primals_28, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf52 = buf51
        del buf51
        triton_poi_fused_convolution_elu_22[grid(4096)](buf52, primals_29, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_29
        buf53 = extern_kernels.convolution(buf52, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf54 = extern_kernels.convolution(buf52, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf55 = reinterpret_tensor(buf47, (4, 512, 4, 4), (8192, 1, 2048, 
            512), 0)
        del buf47
        triton_poi_fused_cat_elu_23[grid(32768)](buf53, primals_31, buf54,
            primals_33, buf55, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del buf53
        del buf54
        del primals_31
        del primals_33
        buf56 = extern_kernels.convolution(buf55, primals_34, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf57 = buf56
        del buf56
        triton_poi_fused_convolution_elu_22[grid(4096)](buf57, primals_35, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_35
        buf58 = extern_kernels.convolution(buf57, primals_36, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf59 = extern_kernels.convolution(buf57, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf60 = reinterpret_tensor(buf46, (4, 512, 4, 4), (8192, 1, 2048, 
            512), 0)
        del buf46
        triton_poi_fused_cat_elu_23[grid(32768)](buf58, primals_37, buf59,
            primals_39, buf60, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del buf58
        del buf59
        del primals_37
        del primals_39
        buf61 = extern_kernels.convolution(buf60, primals_40, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf61, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf62 = buf61
        del buf61
        triton_poi_fused_convolution_elu_22[grid(4096)](buf62, primals_41, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_41
        buf63 = extern_kernels.convolution(buf62, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf63, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf64 = extern_kernels.convolution(buf62, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf64, (4, 256, 4, 4), (4096, 1, 1024, 256))
        buf65 = reinterpret_tensor(buf42, (4, 512, 4, 4), (8192, 1, 2048, 
            512), 0)
        del buf42
        triton_poi_fused_cat_elu_23[grid(32768)](buf63, primals_43, buf64,
            primals_45, buf65, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del buf63
        del buf64
        del primals_43
        del primals_45
        buf66 = extern_kernels.convolution(buf65, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf68 = extern_kernels.convolution(buf65, buf10, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf68, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf70 = extern_kernels.convolution(buf65, buf11, stride=(1, 1),
            padding=(3, 3), dilation=(3, 3), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf70, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf72 = extern_kernels.convolution(buf65, buf12, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf72, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf67 = buf66
        del buf66
        buf69 = buf68
        del buf68
        buf71 = buf70
        del buf70
        buf73 = buf72
        del buf72
        buf74 = reinterpret_tensor(buf41, (4, 512, 4, 4), (8192, 1, 2048, 
            512), 0)
        del buf41
        triton_poi_fused_add_convolution_elu_24[grid(32768)](buf67, buf69,
            buf71, buf73, primals_47, primals_49, primals_51, primals_53,
            buf74, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_47
        del primals_49
        del primals_51
        del primals_53
        buf75 = extern_kernels.convolution(buf74, buf13, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf75, (4, 256, 8, 8), (16384, 1, 2048, 256))
        buf76 = buf75
        del buf75
        triton_poi_fused_convolution_25[grid(65536)](buf76, primals_55, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_55
        buf77 = extern_kernels.convolution(buf48, buf14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf77, (4, 256, 8, 8), (16384, 1, 2048, 256))
        buf78 = buf77
        del buf77
        triton_poi_fused_convolution_elu_26[grid(65536)](buf78, primals_57,
            65536, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_57
        buf79 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_27[grid(8)](buf79, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf80 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_28[grid(8)](buf80, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf81 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused__to_copy_27[grid(8)](buf81, 8, XBLOCK=8, num_warps
            =1, num_stages=1)
        buf82 = empty_strided_cuda((8,), (1,), torch.int64)
        triton_poi_fused_add_clamp_28[grid(8)](buf82, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        buf83 = empty_strided_cuda((8,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_29[grid(8)](buf83, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf84 = reinterpret_tensor(buf30, (4, 256, 8, 8), (16384, 64, 8, 1), 0)
        del buf30
        triton_poi_fused__unsafe_index_add_mul_sub_30[grid(65536)](buf79,
            buf81, buf78, buf82, buf83, buf84, 65536, XBLOCK=512, num_warps
            =4, num_stages=1)
        buf85 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_29[grid(8)](buf85, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf86 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_cat_31[grid(131072)](buf84, buf80, buf81, buf78,
            buf82, buf83, buf85, buf76, buf86, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf87 = extern_kernels.convolution(buf86, buf15, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf88 = buf87
        del buf87
        triton_poi_fused_convolution_elu_32[grid(131072)](buf88, primals_59,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_59
        buf89 = extern_kernels.convolution(buf88, buf16, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf89, (4, 128, 16, 16), (32768, 1, 2048, 128))
        buf90 = buf89
        del buf89
        triton_poi_fused_convolution_elu_33[grid(131072)](buf90, primals_61,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_61
        buf91 = extern_kernels.convolution(buf36, buf17, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf91, (4, 128, 16, 16), (32768, 1, 2048, 128))
        buf92 = buf91
        del buf91
        triton_poi_fused_convolution_34[grid(131072)](buf92, primals_63, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_63
        buf93 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_35[grid(16)](buf93, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf94 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_36[grid(16)](buf94, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf95 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused__to_copy_35[grid(16)](buf95, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf96 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_add_clamp_36[grid(16)](buf96, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf97 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_37[grid(16)](buf97, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf98 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_add_mul_sub_38[grid(131072)](buf93,
            buf95, buf90, buf96, buf97, buf98, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf99 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_arange_clamp_mul_sub_37[grid(16)](buf99, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf100 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_cat_39[grid(262144)](buf92, buf98, buf94, buf95,
            buf90, buf96, buf97, buf99, buf100, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del buf98
        buf101 = extern_kernels.convolution(buf100, buf18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf102 = buf101
        del buf101
        triton_poi_fused_convolution_elu_40[grid(262144)](buf102,
            primals_65, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_65
        buf103 = extern_kernels.convolution(buf102, buf19, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf103, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf104 = buf103
        del buf103
        triton_poi_fused_convolution_41[grid(393216)](buf104, primals_67, 
            393216, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_67
        buf105 = extern_kernels.convolution(buf24, buf20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf105, (4, 96, 32, 32), (98304, 1, 3072, 96))
        buf106 = buf105
        del buf105
        triton_poi_fused_convolution_41[grid(393216)](buf106, primals_69, 
            393216, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_69
        buf107 = empty_strided_cuda((4, 192, 32, 32), (196608, 1, 6144, 192
            ), torch.float32)
        triton_poi_fused_cat_42[grid(786432)](buf106, buf104, buf107, 
            786432, XBLOCK=512, num_warps=8, num_stages=1)
        buf108 = extern_kernels.convolution(buf107, buf21, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 192, 32, 32), (196608, 1, 6144, 192))
        buf109 = buf108
        del buf108
        triton_poi_fused_convolution_elu_43[grid(786432)](buf109,
            primals_71, 786432, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_71
        buf110 = extern_kernels.convolution(buf109, buf22, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf110, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf111 = reinterpret_tensor(buf84, (4, 4, 64, 64), (16384, 4096, 64,
            1), 0)
        del buf84
        triton_poi_fused_convolution_44[grid(16, 4096)](buf110, primals_73,
            buf111, 16, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf110
        del primals_73
    return (buf111, 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, buf9, buf10, buf11,
        buf12, buf13, buf14, buf15, buf16, buf17, buf18, buf19, buf20,
        buf21, buf22, buf24, buf25, buf26, buf28, buf31, buf33, buf36,
        buf37, buf38, buf40, buf43, buf45, buf48, buf49, buf50, buf52,
        buf55, buf57, buf60, buf62, buf65, buf67, buf69, buf71, buf73,
        buf74, buf76, buf78, buf79, buf80, buf81, buf82, buf83, buf85,
        buf86, buf88, buf90, buf92, buf93, buf94, buf95, buf96, buf97,
        buf99, buf100, buf102, buf104, buf106, buf107, buf109)


class Fire(nn.Module):

    def __init__(self, inplanes, squeeze_planes, expand_planes):
        super(Fire, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1,
            stride=1)
        self.relu1 = nn.ELU(inplace=True)
        self.conv2 = nn.Conv2d(squeeze_planes, expand_planes, kernel_size=1,
            stride=1)
        self.conv3 = nn.Conv2d(squeeze_planes, expand_planes, kernel_size=3,
            stride=1, padding=1)
        self.relu2 = nn.ELU(inplace=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        out1 = self.conv2(x)
        out2 = self.conv3(x)
        out = torch.cat([out1, out2], 1)
        out = self.relu2(out)
        return out


class ParallelDilatedConv(nn.Module):

    def __init__(self, inplanes, planes):
        super(ParallelDilatedConv, self).__init__()
        self.dilated_conv_1 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=1, dilation=1)
        self.dilated_conv_2 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=2, dilation=2)
        self.dilated_conv_3 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=3, dilation=3)
        self.dilated_conv_4 = nn.Conv2d(inplanes, planes, kernel_size=3,
            stride=1, padding=4, dilation=4)
        self.relu1 = nn.ELU(inplace=True)
        self.relu2 = nn.ELU(inplace=True)
        self.relu3 = nn.ELU(inplace=True)
        self.relu4 = nn.ELU(inplace=True)

    def forward(self, x):
        out1 = self.dilated_conv_1(x)
        out2 = self.dilated_conv_2(x)
        out3 = self.dilated_conv_3(x)
        out4 = self.dilated_conv_4(x)
        out1 = self.relu1(out1)
        out2 = self.relu2(out2)
        out3 = self.relu3(out3)
        out4 = self.relu4(out4)
        out = out1 + out2 + out3 + out4
        return out


class SQNetNew(nn.Module):

    def __init__(self, classes):
        super().__init__()
        self.num_classes = classes
        self.conv1 = nn.Conv2d(3, 96, kernel_size=3, stride=2, padding=1)
        self.relu1 = nn.ELU(inplace=True)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire1_1 = Fire(96, 16, 64)
        self.fire1_2 = Fire(128, 16, 64)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire2_1 = Fire(128, 32, 128)
        self.fire2_2 = Fire(256, 32, 128)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fire3_1 = Fire(256, 64, 256)
        self.fire3_2 = Fire(512, 64, 256)
        self.fire3_3 = Fire(512, 64, 256)
        self.parallel = ParallelDilatedConv(512, 512)
        self.deconv1 = nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1,
            output_padding=1)
        self.relu2 = nn.ELU(inplace=True)
        self.deconv2 = nn.ConvTranspose2d(512, 128, 3, stride=2, padding=1,
            output_padding=1)
        self.relu3 = nn.ELU(inplace=True)
        self.deconv3 = nn.ConvTranspose2d(256, 96, 3, stride=2, padding=1,
            output_padding=1)
        self.relu4 = nn.ELU(inplace=True)
        self.deconv4 = nn.ConvTranspose2d(192, self.num_classes, 3, stride=
            2, padding=1, output_padding=1)
        self.conv3_1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv2_1 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.conv2_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv1_1 = nn.Conv2d(96, 96, kernel_size=3, stride=1, padding=1)
        self.conv1_2 = nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1)
        self.relu1_1 = nn.ELU(inplace=True)
        self.relu1_2 = nn.ELU(inplace=True)
        self.relu2_1 = nn.ELU(inplace=True)
        self.relu2_2 = nn.ELU(inplace=True)
        self.relu3_1 = nn.ELU(inplace=True)
        self.relu3_2 = nn.ELU(inplace=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.fire1_1.conv1.weight
        primals_5 = self.fire1_1.conv1.bias
        primals_6 = self.fire1_1.conv2.weight
        primals_7 = self.fire1_1.conv2.bias
        primals_8 = self.fire1_1.conv3.weight
        primals_9 = self.fire1_1.conv3.bias
        primals_10 = self.fire1_2.conv1.weight
        primals_11 = self.fire1_2.conv1.bias
        primals_12 = self.fire1_2.conv2.weight
        primals_13 = self.fire1_2.conv2.bias
        primals_14 = self.fire1_2.conv3.weight
        primals_15 = self.fire1_2.conv3.bias
        primals_16 = self.fire2_1.conv1.weight
        primals_17 = self.fire2_1.conv1.bias
        primals_18 = self.fire2_1.conv2.weight
        primals_19 = self.fire2_1.conv2.bias
        primals_20 = self.fire2_1.conv3.weight
        primals_21 = self.fire2_1.conv3.bias
        primals_22 = self.fire2_2.conv1.weight
        primals_23 = self.fire2_2.conv1.bias
        primals_24 = self.fire2_2.conv2.weight
        primals_25 = self.fire2_2.conv2.bias
        primals_26 = self.fire2_2.conv3.weight
        primals_27 = self.fire2_2.conv3.bias
        primals_28 = self.fire3_1.conv1.weight
        primals_29 = self.fire3_1.conv1.bias
        primals_30 = self.fire3_1.conv2.weight
        primals_31 = self.fire3_1.conv2.bias
        primals_32 = self.fire3_1.conv3.weight
        primals_33 = self.fire3_1.conv3.bias
        primals_34 = self.fire3_2.conv1.weight
        primals_35 = self.fire3_2.conv1.bias
        primals_36 = self.fire3_2.conv2.weight
        primals_37 = self.fire3_2.conv2.bias
        primals_38 = self.fire3_2.conv3.weight
        primals_39 = self.fire3_2.conv3.bias
        primals_40 = self.fire3_3.conv1.weight
        primals_41 = self.fire3_3.conv1.bias
        primals_42 = self.fire3_3.conv2.weight
        primals_43 = self.fire3_3.conv2.bias
        primals_44 = self.fire3_3.conv3.weight
        primals_45 = self.fire3_3.conv3.bias
        primals_46 = self.parallel.dilated_conv_1.weight
        primals_47 = self.parallel.dilated_conv_1.bias
        primals_48 = self.parallel.dilated_conv_2.weight
        primals_49 = self.parallel.dilated_conv_2.bias
        primals_50 = self.parallel.dilated_conv_3.weight
        primals_51 = self.parallel.dilated_conv_3.bias
        primals_52 = self.parallel.dilated_conv_4.weight
        primals_53 = self.parallel.dilated_conv_4.bias
        primals_54 = self.deconv1.weight
        primals_55 = self.deconv1.bias
        primals_60 = self.deconv2.weight
        primals_61 = self.deconv2.bias
        primals_66 = self.deconv3.weight
        primals_67 = self.deconv3.bias
        primals_72 = self.deconv4.weight
        primals_73 = self.deconv4.bias
        primals_56 = self.conv3_1.weight
        primals_57 = self.conv3_1.bias
        primals_58 = self.conv3_2.weight
        primals_59 = self.conv3_2.bias
        primals_62 = self.conv2_1.weight
        primals_63 = self.conv2_1.bias
        primals_64 = self.conv2_2.weight
        primals_65 = self.conv2_2.bias
        primals_68 = self.conv1_1.weight
        primals_69 = self.conv1_1.bias
        primals_70 = self.conv1_2.weight
        primals_71 = self.conv1_2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59,
            primals_60, primals_61, primals_62, primals_63, primals_64,
            primals_65, primals_66, primals_67, primals_68, primals_69,
            primals_70, primals_71, primals_72, primals_73])
        return output[0]
