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

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


# kernel path: runs/run_shard_6/inductor_cache/4h/c4h4tqg7thvgd4sjytpdlbglghgipeugjwt6y4mayhdt7cqh7yp7.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=[32, 32], 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), 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 = 24
    xnumel = 25
    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 + (25*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (75*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7a/c7af3agsnw76zx5luwpaougjaunb6etthvofkg6f5vde7f7p6vuc.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, 32768], 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 = 20736
    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 + (20736*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (62208*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4x/c4x6dil7prj6eiyet5rpqui2yirjp2i4thkmvixjhlwd2wngl2qp.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=[128, 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 = 128
    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 % 8
    y1 = (yindex // 8)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (8*x2) + (72*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/sn/csn6lfjgrf73pf3dzowbyzudbrbljrwdsbt65oxi3il47qmnfny7.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=[256, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 256
    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 % 16
    y1 = (yindex // 16)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (16*x2) + (144*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/3b/c3bcocaijyicw7ikzgxf7hofnapq7h53a4yytw7oxv5pwdkxcaea.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=[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_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 = 384
    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 % 16
    y1 = (yindex // 16)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (16*x2) + (144*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/m4/cm4djqg7x2xfxlxhrdeap55xpbjpilw2o7kctcsiermdt25v3ex7.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=[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_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 = 576
    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 % 24
    y1 = (yindex // 24)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (24*x2) + (216*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/3y/c3yakyj3fu4c5dfkiwj453gz34pg2lz3fbbc4no6w3obfyj5scsb.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=[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_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 = 960
    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 % 24
    y1 = (yindex // 24)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (24*x2) + (216*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gd/cgdpfeairwgkycakcf4kpn2c7d2duvtpqac2r2jsh545xa226li3.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=[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_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 = 3200
    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 % 40
    y1 = (yindex // 40)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (40*x2) + (360*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pd/cpdqbonooqc47dlq3pylf5hgg3bpgatyijoeftpgcavvvytc7nhc.py
# Topologically Sorted Source Nodes: [conv2d, prelu], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d => convolution
#   prelu => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_8 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_8(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 156800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/o6/co65hryhmydqwomplqcp5zjqfztasohoxms3435othselzw3aszm.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where, [2, 2], [2, 2], [0, 0], True), kwargs = {})
triton_poi_fused_avg_pool2d_9 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[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_avg_pool2d_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_avg_pool2d_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 39200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 35
    x2 = (xindex // 280)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1) + (1120*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (8 + x0 + (16*x1) + (1120*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (560 + x0 + (16*x1) + (1120*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (568 + x0 + (16*x1) + (1120*x2)), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ii/ciiasyyhp4puzpwt7xshhzi56ijwhofkmw6it5wl4rsskdpiekui.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   x_1 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution_1 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d, %primals_5, %primals_6, [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_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_1), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_10 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_10(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 69696
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yu/cyui43bftuv4e4bvu4wftozzfz6sfu3y3lic267aldqxzxplqr5r.py
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_2 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_7, %primals_8, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_11 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_11(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/do/cdo4u3dmfya5bticcx4khuzpfvgrwahyutqsw7tesmoaegdawljp.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_3 => avg_pool2d_1
# Graph fragment:
#   %avg_pool2d_1 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_2, [2, 2], [2, 2], [0, 0], True), kwargs = {})
triton_poi_fused_avg_pool2d_12 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[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_avg_pool2d_12', '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_avg_pool2d_12(in_ptr0, out_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 // 256) % 16
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x3 = (xindex // 4096)
    x6 = xindex
    tmp0 = 2*x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 31, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = 2*x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (x0 + (32*x1) + (992*x2) + (15376*x3)), tmp10, other=0.0)
    tmp12 = 1 + (2*x1)
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (16 + x0 + (32*x1) + (992*x2) + (15376*x3)), tmp16, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + (2*x2)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (496 + x0 + (32*x1) + (992*x2) + (15376*x3)), tmp23, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (512 + x0 + (32*x1) + (992*x2) + (15376*x3)), tmp26, other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = (((31) * ((31) <= (2 + (2*x1))) + (2 + (2*x1)) * ((2 + (2*x1)) < (31)))*((31) * ((31) <= (2 + (2*x2))) + (2 + (2*x2)) * ((2 + (2*x2)) < (31)))) + ((-2)*x1*((31) * ((31) <= (2 + (2*x2))) + (2 + (2*x2)) * ((2 + (2*x2)) < (31)))) + ((-2)*x2*((31) * ((31) <= (2 + (2*x1))) + (2 + (2*x1)) * ((2 + (2*x1)) < (31)))) + (4*x1*x2)
    tmp30 = tmp28 / tmp29
    tl.store(out_ptr0 + (x6), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/kz/ckzg22ugjwlfn6ynvayv26zfmrvyijdgmy4v2dzqpklowq5q3ndd.py
# Topologically Sorted Source Nodes: [conv2d_3, x_4], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   x_4 => gt_3, mul_3, where_3
# Graph fragment:
#   %convolution_3 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_1, %primals_9, %primals_10, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_3), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_13 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_13(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 18816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 24
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/os/cosq6s4jzetqtc26xrs6f3vm3jxscatlp2jkx5l3hcc5dj5slza2.py
# Topologically Sorted Source Nodes: [conv2d_4, x_5], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   x_5 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_3, %primals_11, %primals_12, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_4, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_4), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_14 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_14(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 13824
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 24
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jo/cjok2cvjkd3lwixr6ary5mrexxymldy43v4nr47kblhcbl7b45uv.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_6 => avg_pool2d_2
# Graph fragment:
#   %avg_pool2d_2 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%where_4, [2, 2], [2, 2], [0, 0], True), kwargs = {})
triton_poi_fused_avg_pool2d_15 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[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_avg_pool2d_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_avg_pool2d_15(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 24
    x1 = (xindex // 24) % 6
    x2 = (xindex // 144)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (48*x1) + (576*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (24 + x0 + (48*x1) + (576*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (288 + x0 + (48*x1) + (576*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (312 + x0 + (48*x1) + (576*x2)), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/v6/cv6ahvog4uaderovx2yykf5ldyaioitthsrxvbpcoznt4tp52ffj.py
# Topologically Sorted Source Nodes: [conv2d_5, x_7], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
#   x_7 => gt_5, mul_5, where_5
# Graph fragment:
#   %convolution_5 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%avg_pool2d_2, %primals_13, %primals_14, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_5), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_16 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_16', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_16(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5x/c5xd2ezwlervap7ojkon47yxwjxar3pp2udswkyshgwzecgmyj36.py
# Topologically Sorted Source Nodes: [conv2d_6, x_8], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   x_8 => gt_6, mul_6, where_6
# Graph fragment:
#   %convolution_6 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where_5, %primals_15, %primals_16, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_6 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_6, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution_6), kwargs = {})
#   %where_6 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %convolution_6, %mul_6), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_17 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[256, 128], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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__prelu_kernel_convolution_17', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_17(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 144
    xnumel = 80
    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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 36
    y3 = (yindex // 36)
    tmp0 = tl.load(in_out_ptr0 + (x1 + (80*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, YBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x1 + (80*y0)), tmp2, xmask & ymask)
    tl.store(out_ptr0 + (y2 + (36*x1) + (2880*y3)), tmp8, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uz/cuzmvsuntaq7m4buwjzonzozqhgttm3fda3h57t6cz42lxsykfsk.py
# Topologically Sorted Source Nodes: [ip_1], Original ATen: [aten._prelu_kernel]
# Source node to ATen node mapping:
#   ip_1 => gt_7, mul_7, where_7
# Graph fragment:
#   %gt_7 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%addmm, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_8, %addmm), kwargs = {})
#   %where_7 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_7, %addmm, %mul_7), kwargs = {})
triton_poi_fused__prelu_kernel_18 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[2048], 
    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__prelu_kernel_18', '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__prelu_kernel_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp4 * tmp0
    tmp6 = tl.where(tmp2, tmp0, tmp5)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22 = args
    args.clear()
    assert_size_stride(primals_1, (8, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 144, 144), (62208, 20736, 144, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_6, (16, ), (1, ))
    assert_size_stride(primals_7, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_8, (16, ), (1, ))
    assert_size_stride(primals_9, (24, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_10, (24, ), (1, ))
    assert_size_stride(primals_11, (24, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_12, (24, ), (1, ))
    assert_size_stride(primals_13, (40, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_14, (40, ), (1, ))
    assert_size_stride(primals_15, (80, 40, 3, 3), (360, 9, 3, 1))
    assert_size_stride(primals_16, (80, ), (1, ))
    assert_size_stride(primals_17, (128, 1280), (1280, 1))
    assert_size_stride(primals_18, (128, ), (1, ))
    assert_size_stride(primals_19, (128, 128), (128, 1))
    assert_size_stride(primals_20, (128, ), (1, ))
    assert_size_stride(primals_21, (42, 128), (128, 1))
    assert_size_stride(primals_22, (42, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 3, 5, 5), (75, 1, 15, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 24, 25, grid=grid(24, 25), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 144, 144), (62208, 1, 432, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 12, 20736, grid=grid(12, 20736), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((16, 8, 3, 3), (72, 1, 24, 8), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_5, buf2, 128, 9, grid=grid(128, 9), stream=stream0)
        del primals_5
        buf3 = empty_strided_cuda((16, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_7, buf3, 256, 9, grid=grid(256, 9), stream=stream0)
        del primals_7
        buf4 = empty_strided_cuda((24, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_9, buf4, 384, 9, grid=grid(384, 9), stream=stream0)
        del primals_9
        buf5 = empty_strided_cuda((24, 24, 3, 3), (216, 1, 72, 24), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_11, buf5, 576, 9, grid=grid(576, 9), stream=stream0)
        del primals_11
        buf6 = empty_strided_cuda((40, 24, 3, 3), (216, 1, 72, 24), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_13, buf6, 960, 9, grid=grid(960, 9), stream=stream0)
        del primals_13
        buf7 = empty_strided_cuda((80, 40, 3, 3), (360, 1, 120, 40), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_7.run(primals_15, buf7, 3200, 9, grid=grid(3200, 9), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 8, 70, 70), (39200, 1, 560, 8))
        buf9 = buf8; del buf8  # reuse
        buf10 = empty_strided_cuda((4, 8, 70, 70), (39200, 1, 560, 8), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, prelu], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_8.run(buf9, primals_2, primals_4, buf10, 156800, grid=grid(156800), stream=stream0)
        del primals_2
        buf11 = empty_strided_cuda((4, 8, 35, 35), (9800, 1, 280, 8), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_9.run(buf10, buf11, 39200, grid=grid(39200), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 16, 33, 33), (17424, 1, 528, 16))
        buf13 = buf12; del buf12  # reuse
        buf14 = empty_strided_cuda((4, 16, 33, 33), (17424, 1, 528, 16), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_10.run(buf13, primals_6, primals_4, buf14, 69696, grid=grid(69696), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 16, 31, 31), (15376, 1, 496, 16))
        buf16 = buf15; del buf15  # reuse
        buf17 = empty_strided_cuda((4, 16, 31, 31), (15376, 1, 496, 16), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_11.run(buf16, primals_8, primals_4, buf17, 61504, grid=grid(61504), stream=stream0)
        del primals_8
        buf18 = empty_strided_cuda((4, 16, 16, 16), (4096, 1, 256, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_12.run(buf17, buf18, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 24, 14, 14), (4704, 1, 336, 24))
        buf20 = buf19; del buf19  # reuse
        buf21 = empty_strided_cuda((4, 24, 14, 14), (4704, 1, 336, 24), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_3, x_4], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_13.run(buf20, primals_10, primals_4, buf21, 18816, grid=grid(18816), stream=stream0)
        del primals_10
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 24, 12, 12), (3456, 1, 288, 24))
        buf23 = buf22; del buf22  # reuse
        buf24 = empty_strided_cuda((4, 24, 12, 12), (3456, 1, 288, 24), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_4, x_5], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_14.run(buf23, primals_12, primals_4, buf24, 13824, grid=grid(13824), stream=stream0)
        del primals_12
        buf25 = empty_strided_cuda((4, 24, 6, 6), (864, 1, 144, 24), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_15.run(buf24, buf25, 3456, grid=grid(3456), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 40, 6, 6), (1440, 1, 240, 40))
        buf27 = buf26; del buf26  # reuse
        buf28 = empty_strided_cuda((4, 40, 6, 6), (1440, 1, 240, 40), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_5, x_7], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_16.run(buf27, primals_14, primals_4, buf28, 5760, grid=grid(5760), stream=stream0)
        del primals_14
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf29 = extern_kernels.convolution(buf28, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 80, 6, 6), (2880, 1, 480, 80))
        buf30 = buf29; del buf29  # reuse
        buf31 = empty_strided_cuda((4, 80, 6, 6), (2880, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_6, x_8], Original ATen: [aten.convolution, aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_convolution_17.run(buf30, primals_16, primals_4, buf31, 144, 80, grid=grid(144, 80), stream=stream0)
        del primals_16
        buf32 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf31, (9, 1280), (1280, 1), 0), reinterpret_tensor(primals_17, (1280, 128), (1, 1280), 0), alpha=1, beta=1, out=buf32)
        del primals_18
        buf33 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ip_1], Original ATen: [aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_18.run(buf32, primals_4, buf33, 1152, grid=grid(1152), stream=stream0)
        buf34 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_20, buf33, reinterpret_tensor(primals_19, (128, 128), (1, 128), 0), alpha=1, beta=1, out=buf34)
        del primals_20
        buf35 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ip_2], Original ATen: [aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_18.run(buf34, primals_4, buf35, 1152, grid=grid(1152), stream=stream0)
        buf36 = empty_strided_cuda((9, 42), (42, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ip_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_22, buf35, reinterpret_tensor(primals_21, (128, 42), (1, 128), 0), alpha=1, beta=1, out=buf36)
        del primals_22
    return (buf36, buf0, buf1, primals_4, buf2, buf3, buf4, buf5, buf6, buf7, buf9, buf10, buf11, buf13, buf14, buf16, buf17, buf18, buf20, buf21, buf23, buf24, buf25, buf27, buf28, buf30, reinterpret_tensor(buf31, (9, 1280), (1280, 1), 0), buf32, buf33, buf34, buf35, primals_21, primals_19, primals_17, )


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((8, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 144, 144), (62208, 20736, 144, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((24, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((24, 24, 3, 3), (216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((40, 24, 3, 3), (216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((40, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((80, 40, 3, 3), (360, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((80, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, 1280), (1280, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((42, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((42, ), (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])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn


class MyNet(nn.Module):

    def __init__(self):
        super(MyNet, self).__init__()
        self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size
            =5, stride=2, padding=0)
        self.conv2_1 = nn.Conv2d(8, 16, 3, 1, 0)
        self.conv2_2 = nn.Conv2d(16, 16, 3, 1, 0)
        self.conv3_1 = nn.Conv2d(16, 24, 3, 1, 0)
        self.conv3_2 = nn.Conv2d(24, 24, 3, 1, 0)
        self.conv4_1 = nn.Conv2d(24, 40, 3, 1, 1)
        self.conv4_2 = nn.Conv2d(40, 80, 3, 1, 1)
        self.ip1 = nn.Linear(80 * 4 * 4, 128)
        self.ip2 = nn.Linear(128, 128)
        self.ip3 = nn.Linear(128, 42)
        self.prelu = nn.PReLU()
        self.avg_pool = nn.AvgPool2d(2, 2, ceil_mode=True)

    def forward(self, x):
        x = self.avg_pool(self.prelu(self.conv1_1(x)))
        x = self.prelu(self.conv2_1(x))
        x = self.prelu(self.conv2_2(x))
        x = self.avg_pool(x)
        x = self.prelu(self.conv3_1(x))
        x = self.prelu(self.conv3_2(x))
        x = self.avg_pool(x)
        x = self.prelu(self.conv4_1(x))
        x = self.prelu(self.conv4_2(x))
        ip = x.view(-1, 4 * 4 * 80)
        ip = self.prelu(self.ip1(ip))
        ip = self.prelu(self.ip2(ip))
        ip = self.ip3(ip)
        return ip


def get_inputs():
    return [torch.rand([4, 3, 144, 144])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 = 24
    xnumel = 25
    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 + 25 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 75 * 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
    xnumel = 20736
    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 + 20736 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 62208 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    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 % 8
    y1 = yindex // 8
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 8 * x2 + 72 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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 % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 16 * x2 + 144 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 384
    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 % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 16 * x2 + 144 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 576
    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 % 24
    y1 = yindex // 24
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 24 * x2 + 216 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 960
    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 % 24
    y1 = yindex // 24
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 24 * x2 + 216 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 3200
    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 % 40
    y1 = yindex // 40
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 40 * x2 + 360 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_8(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 156800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 39200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 35
    x2 = xindex // 280
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1 + 1120 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (8 + x0 + 16 * x1 + 1120 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (560 + x0 + 16 * x1 + 1120 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (568 + x0 + 16 * x1 + 1120 * x2), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_10(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 69696
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_11(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 256 % 16
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x3 = xindex // 4096
    x6 = xindex
    tmp0 = 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 31, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (x0 + 32 * x1 + 992 * x2 + 15376 * x3), tmp10,
        other=0.0)
    tmp12 = 1 + 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (16 + x0 + 32 * x1 + 992 * x2 + 15376 * x3),
        tmp16, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + 2 * x2
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (496 + x0 + 32 * x1 + 992 * x2 + 15376 * x3),
        tmp23, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (512 + x0 + 32 * x1 + 992 * x2 + 15376 * x3),
        tmp26, other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = (31 * (31 <= 2 + 2 * x1) + (2 + 2 * x1) * (2 + 2 * x1 < 31)) * (
        31 * (31 <= 2 + 2 * x2) + (2 + 2 * x2) * (2 + 2 * x2 < 31)
        ) + -2 * x1 * (31 * (31 <= 2 + 2 * x2) + (2 + 2 * x2) * (2 + 2 * x2 <
        31)) + -2 * x2 * (31 * (31 <= 2 + 2 * x1) + (2 + 2 * x1) * (2 + 2 *
        x1 < 31)) + 4 * x1 * x2
    tmp30 = tmp28 / tmp29
    tl.store(out_ptr0 + x6, tmp30, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_13(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 18816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 24
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_14(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 13824
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 24
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_15(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 3456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 24
    x1 = xindex // 24 % 6
    x2 = xindex // 144
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 48 * x1 + 576 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (24 + x0 + 48 * x1 + 576 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (288 + x0 + 48 * x1 + 576 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (312 + x0 + 48 * x1 + 576 * x2), xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_16(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_17(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 144
    xnumel = 80
    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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 36
    y3 = yindex // 36
    tmp0 = tl.load(in_out_ptr0 + (x1 + 80 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, YBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x1 + 80 * y0), tmp2, xmask & ymask)
    tl.store(out_ptr0 + (y2 + 36 * x1 + 2880 * y3), tmp8, xmask & ymask)


@triton.jit
def triton_poi_fused__prelu_kernel_18(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp4 * tmp0
    tmp6 = tl.where(tmp2, tmp0, tmp5)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22) = args
    args.clear()
    assert_size_stride(primals_1, (8, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 3, 144, 144), (62208, 20736, 144, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_8, (16,), (1,))
    assert_size_stride(primals_9, (24, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_10, (24,), (1,))
    assert_size_stride(primals_11, (24, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_12, (24,), (1,))
    assert_size_stride(primals_13, (40, 24, 3, 3), (216, 9, 3, 1))
    assert_size_stride(primals_14, (40,), (1,))
    assert_size_stride(primals_15, (80, 40, 3, 3), (360, 9, 3, 1))
    assert_size_stride(primals_16, (80,), (1,))
    assert_size_stride(primals_17, (128, 1280), (1280, 1))
    assert_size_stride(primals_18, (128,), (1,))
    assert_size_stride(primals_19, (128, 128), (128, 1))
    assert_size_stride(primals_20, (128,), (1,))
    assert_size_stride(primals_21, (42, 128), (128, 1))
    assert_size_stride(primals_22, (42,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 3, 5, 5), (75, 1, 15, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(24, 25)](primals_1, buf0, 24, 25, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 144, 144), (62208, 1, 432, 3),
            torch.float32)
        triton_poi_fused_1[grid(12, 20736)](primals_3, buf1, 12, 20736,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((16, 8, 3, 3), (72, 1, 24, 8), torch.float32)
        triton_poi_fused_2[grid(128, 9)](primals_5, buf2, 128, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf3 = empty_strided_cuda((16, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_3[grid(256, 9)](primals_7, buf3, 256, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_7
        buf4 = empty_strided_cuda((24, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_4[grid(384, 9)](primals_9, buf4, 384, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_9
        buf5 = empty_strided_cuda((24, 24, 3, 3), (216, 1, 72, 24), torch.
            float32)
        triton_poi_fused_5[grid(576, 9)](primals_11, buf5, 576, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_11
        buf6 = empty_strided_cuda((40, 24, 3, 3), (216, 1, 72, 24), torch.
            float32)
        triton_poi_fused_6[grid(960, 9)](primals_13, buf6, 960, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_13
        buf7 = empty_strided_cuda((80, 40, 3, 3), (360, 1, 120, 40), torch.
            float32)
        triton_poi_fused_7[grid(3200, 9)](primals_15, buf7, 3200, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_15
        buf8 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 8, 70, 70), (39200, 1, 560, 8))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 8, 70, 70), (39200, 1, 560, 8),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_8[grid(156800)](buf9,
            primals_2, primals_4, buf10, 156800, XBLOCK=512, num_warps=8,
            num_stages=1)
        del primals_2
        buf11 = empty_strided_cuda((4, 8, 35, 35), (9800, 1, 280, 8), torch
            .float32)
        triton_poi_fused_avg_pool2d_9[grid(39200)](buf10, buf11, 39200,
            XBLOCK=512, num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, buf2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 16, 33, 33), (17424, 1, 528, 16))
        buf13 = buf12
        del buf12
        buf14 = empty_strided_cuda((4, 16, 33, 33), (17424, 1, 528, 16),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_10[grid(69696)](buf13,
            primals_6, primals_4, buf14, 69696, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_6
        buf15 = extern_kernels.convolution(buf14, buf3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 16, 31, 31), (15376, 1, 496, 16))
        buf16 = buf15
        del buf15
        buf17 = empty_strided_cuda((4, 16, 31, 31), (15376, 1, 496, 16),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_11[grid(61504)](buf16,
            primals_8, primals_4, buf17, 61504, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_8
        buf18 = empty_strided_cuda((4, 16, 16, 16), (4096, 1, 256, 16),
            torch.float32)
        triton_poi_fused_avg_pool2d_12[grid(16384)](buf17, buf18, 16384,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf19 = extern_kernels.convolution(buf18, buf4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 24, 14, 14), (4704, 1, 336, 24))
        buf20 = buf19
        del buf19
        buf21 = empty_strided_cuda((4, 24, 14, 14), (4704, 1, 336, 24),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_13[grid(18816)](buf20,
            primals_10, primals_4, buf21, 18816, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_10
        buf22 = extern_kernels.convolution(buf21, buf5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 24, 12, 12), (3456, 1, 288, 24))
        buf23 = buf22
        del buf22
        buf24 = empty_strided_cuda((4, 24, 12, 12), (3456, 1, 288, 24),
            torch.float32)
        triton_poi_fused__prelu_kernel_convolution_14[grid(13824)](buf23,
            primals_12, primals_4, buf24, 13824, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_12
        buf25 = empty_strided_cuda((4, 24, 6, 6), (864, 1, 144, 24), torch.
            float32)
        triton_poi_fused_avg_pool2d_15[grid(3456)](buf24, buf25, 3456,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 40, 6, 6), (1440, 1, 240, 40))
        buf27 = buf26
        del buf26
        buf28 = empty_strided_cuda((4, 40, 6, 6), (1440, 1, 240, 40), torch
            .float32)
        triton_poi_fused__prelu_kernel_convolution_16[grid(5760)](buf27,
            primals_14, primals_4, buf28, 5760, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_14
        buf29 = extern_kernels.convolution(buf28, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 80, 6, 6), (2880, 1, 480, 80))
        buf30 = buf29
        del buf29
        buf31 = empty_strided_cuda((4, 80, 6, 6), (2880, 36, 6, 1), torch.
            float32)
        triton_poi_fused__prelu_kernel_convolution_17[grid(144, 80)](buf30,
            primals_16, primals_4, buf31, 144, 80, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del primals_16
        buf32 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf31, (9, 1280
            ), (1280, 1), 0), reinterpret_tensor(primals_17, (1280, 128), (
            1, 1280), 0), alpha=1, beta=1, out=buf32)
        del primals_18
        buf33 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        triton_poi_fused__prelu_kernel_18[grid(1152)](buf32, primals_4,
            buf33, 1152, XBLOCK=256, num_warps=4, num_stages=1)
        buf34 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_20, buf33, reinterpret_tensor(
            primals_19, (128, 128), (1, 128), 0), alpha=1, beta=1, out=buf34)
        del primals_20
        buf35 = empty_strided_cuda((9, 128), (128, 1), torch.float32)
        triton_poi_fused__prelu_kernel_18[grid(1152)](buf34, primals_4,
            buf35, 1152, XBLOCK=256, num_warps=4, num_stages=1)
        buf36 = empty_strided_cuda((9, 42), (42, 1), torch.float32)
        extern_kernels.addmm(primals_22, buf35, reinterpret_tensor(
            primals_21, (128, 42), (1, 128), 0), alpha=1, beta=1, out=buf36)
        del primals_22
    return (buf36, buf0, buf1, primals_4, buf2, buf3, buf4, buf5, buf6,
        buf7, buf9, buf10, buf11, buf13, buf14, buf16, buf17, buf18, buf20,
        buf21, buf23, buf24, buf25, buf27, buf28, buf30, reinterpret_tensor
        (buf31, (9, 1280), (1280, 1), 0), buf32, buf33, buf34, buf35,
        primals_21, primals_19, primals_17)


class MyNetNew(nn.Module):

    def __init__(self):
        super(MyNetNew, self).__init__()
        self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size
            =5, stride=2, padding=0)
        self.conv2_1 = nn.Conv2d(8, 16, 3, 1, 0)
        self.conv2_2 = nn.Conv2d(16, 16, 3, 1, 0)
        self.conv3_1 = nn.Conv2d(16, 24, 3, 1, 0)
        self.conv3_2 = nn.Conv2d(24, 24, 3, 1, 0)
        self.conv4_1 = nn.Conv2d(24, 40, 3, 1, 1)
        self.conv4_2 = nn.Conv2d(40, 80, 3, 1, 1)
        self.ip1 = nn.Linear(80 * 4 * 4, 128)
        self.ip2 = nn.Linear(128, 128)
        self.ip3 = nn.Linear(128, 42)
        self.prelu = nn.PReLU()
        self.avg_pool = nn.AvgPool2d(2, 2, ceil_mode=True)

    def forward(self, input_0):
        primals_1 = self.conv1_1.weight
        primals_2 = self.conv1_1.bias
        primals_5 = self.conv2_1.weight
        primals_6 = self.conv2_1.bias
        primals_7 = self.conv2_2.weight
        primals_8 = self.conv2_2.bias
        primals_9 = self.conv3_1.weight
        primals_10 = self.conv3_1.bias
        primals_11 = self.conv3_2.weight
        primals_12 = self.conv3_2.bias
        primals_13 = self.conv4_1.weight
        primals_14 = self.conv4_1.bias
        primals_15 = self.conv4_2.weight
        primals_16 = self.conv4_2.bias
        primals_17 = self.ip1.weight
        primals_18 = self.ip1.bias
        primals_19 = self.ip2.weight
        primals_20 = self.ip2.bias
        primals_21 = self.ip3.weight
        primals_22 = self.ip3.bias
        primals_4 = self.prelu.weight
        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])
        return output[0]
