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

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


# kernel path: runs/run_shard_7/inductor_cache/n5/cn5ihbr7hyt2dxukfqr27vl7atwnwccevwjijotxny5mi3asb4jf.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=[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_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 = 80
    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 % 5
    y1 = (yindex // 5)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (5*x2) + (45*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ht/chtmbgf6b7ptt7rqj5vjjhvstukolwrlihyaxypt727qskie7rzo.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=[32, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 20
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 5
    y1 = (yindex // 5)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (5*x2) + (20480*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/33/c33h7oa7bwaolrz6x7pou267xerujig6qx2chnlgozpghlackaon.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=[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_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 = 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_7/inductor_cache/7v/c7vkuh3vgeafo6rqr2fstktnfpdnlk6u5m3tfup4tnbid26ionkm.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=[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_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 = 512
    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_7/inductor_cache/a6/ca673na7vq3mgudtku5svyuyh2rc2snm32rmycazpcdwelcsirpm.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=[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_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 = 1024
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (32*x2) + (288*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/oe/coefioksk274ahsx5xw4xk5oiijr3n6skxyyxw4lbgqaf443a276.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=[2048, 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 = 2048
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (32*x2) + (288*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fq/cfqqokaqc6in5vvglkxliqy52newlyeztcdbiq6tloce2xqcjt2j.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=[4096, 4], 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 = 4096
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (256*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yd/cydqyi3yfiwrbnrg7nrcodsva6zsemaymgjrqi4aailyyotyxcw2.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=[8192, 4], 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 = 8192
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (256*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/22/c22j6a6dnynl6jertdfz7ci2ntsqkzk3qbvwfjyx5wuaizrcdt5w.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nn/cnnrha2fherbxf4u4ol3reswxwrhd2on7n4ktcvs6jj5lim7f4hb.py
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_2 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_9 = async_compile.triton('triton_poi_fused_convolution_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/w7/cw7dnjyhnkjxcocockmfeyrvpfho55riiy774j436rl7rxxzpszn.py
# Topologically Sorted Source Nodes: [conv2d_5, x_5], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_5 => convolution_5
#   x_5 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_12, %primals_13, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
triton_poi_fused_convolution_relu_10 = async_compile.triton('triton_poi_fused_convolution_relu_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 57600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tv/ctvgh7xmtvqkyzfxxmw5do6felldwomgroa3covpuni256w4uf7g.py
# Topologically Sorted Source Nodes: [conv2d_6, x_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_6 => convolution_6
#   x_6 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_14, %primals_15, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
triton_poi_fused_convolution_relu_11 = async_compile.triton('triton_poi_fused_convolution_relu_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7r/c7ra2zy2fyuprdpyfrgqv6xytdz4ftt72qzqprrs3ws6b64zu4jn.py
# Topologically Sorted Source Nodes: [conv2d_7, x_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_7 => convolution_7
#   x_7 => relu_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_6, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_12 = async_compile.triton('triton_poi_fused_convolution_relu_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 73984
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/za/cza246hp3d4a4z5szskt364isrffbvnufdwmsjo6vmunmevlftwa.py
# Topologically Sorted Source Nodes: [conv2d_8, x_8], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_8 => convolution_8
#   x_8 => relu_8
# Graph fragment:
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_18, %primals_19, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
triton_poi_fused_convolution_relu_13 = async_compile.triton('triton_poi_fused_convolution_relu_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5y/c5yor5momrltl7zyqthobyuonhj6wwvwbms5ool624cwv4if4m7j.py
# Topologically Sorted Source Nodes: [conv2d_11, x_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   x_11 => relu_11
# Graph fragment:
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_10, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[512, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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_convolution_relu_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 512
    xnumel = 64
    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
    y0 = yindex % 128
    y1 = (yindex // 128)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (128*x2) + (8192*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + (64*y3)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (128*x2) + (8192*y1)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/al/calzyyikft3iztavfaye4byheoulhcsb7ceni2j2xwv44qsedsbt.py
# Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   tanh => tanh
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_27), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_tanh_15 = async_compile.triton('triton_poi_fused_tanh_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=[256], 
    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_tanh_15', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_tanh_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27 = args
    args.clear()
    assert_size_stride(primals_1, (16, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 5, 64, 64), (20480, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32, ), (1, ))
    assert_size_stride(primals_12, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (64, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (64, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_17, (64, ), (1, ))
    assert_size_stride(primals_18, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (1, 128), (128, 1))
    assert_size_stride(primals_27, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 5, 3, 3), (45, 1, 15, 5), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 80, 9, grid=grid(80, 9), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 5, 64, 64), (20480, 1, 320, 5), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 20, 4096, grid=grid(20, 4096), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((16, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_4, buf2, 256, 9, grid=grid(256, 9), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((32, 16, 3, 3), (144, 1, 48, 16), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_6, buf3, 512, 9, grid=grid(512, 9), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((32, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_8, buf4, 1024, 9, grid=grid(1024, 9), stream=stream0)
        del primals_8
        buf5 = empty_strided_cuda((32, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_10, buf5, 1024, 9, grid=grid(1024, 9), stream=stream0)
        del primals_10
        buf6 = empty_strided_cuda((64, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(primals_12, buf6, 2048, 9, grid=grid(2048, 9), stream=stream0)
        del primals_12
        buf7 = empty_strided_cuda((64, 64, 2, 2), (256, 1, 128, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_14, buf7, 4096, 4, grid=grid(4096, 4), stream=stream0)
        del primals_14
        buf8 = empty_strided_cuda((64, 64, 2, 2), (256, 1, 128, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(primals_16, buf8, 4096, 4, grid=grid(4096, 4), stream=stream0)
        del primals_16
        buf9 = empty_strided_cuda((128, 64, 2, 2), (256, 1, 128, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_7.run(primals_18, buf9, 8192, 4, grid=grid(8192, 4), stream=stream0)
        del primals_18
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 1, 1024, 16))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf11, primals_2, 262144, grid=grid(262144), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 16, 64, 64), (65536, 1, 1024, 16))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf13, primals_5, 262144, grid=grid(262144), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, buf3, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf15, primals_7, 123008, grid=grid(123008), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf17, primals_9, 123008, grid=grid(123008), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, x_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf19, primals_11, 123008, grid=grid(123008), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, buf6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 15, 15), (14400, 1, 960, 64))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, x_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_10.run(buf21, primals_13, 57600, grid=grid(57600), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, x_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_11.run(buf23, primals_15, 65536, grid=grid(65536), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, buf8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 17, 17), (18496, 1, 1088, 64))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf25, primals_17, 73984, grid=grid(73984), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, buf9, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, x_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf27, primals_19, 32768, grid=grid(32768), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf29 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, x_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf29, primals_21, 32768, grid=grid(32768), stream=stream0)
        del primals_21
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10, x_10], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf31, primals_23, 32768, grid=grid(32768), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf32 = extern_kernels.convolution(buf31, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf33 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
        buf36 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_11, x_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_14.run(buf32, primals_25, buf33, buf36, 512, 64, grid=grid(512, 64), stream=stream0)
        del buf32
        del primals_25
        buf34 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf33, (256, 128), (128, 1), 0), reinterpret_tensor(primals_26, (128, 1), (1, 128), 0), out=buf34)
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_15.run(buf35, primals_27, 256, grid=grid(256), stream=stream0)
        del primals_27
    return (buf35, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, primals_20, primals_22, primals_24, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf29, buf31, reinterpret_tensor(buf33, (256, 128), (128, 1), 0), buf35, primals_26, buf36, )


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((16, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 5, 64, 64), (20480, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((64, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((1, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27])
    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
import torch.nn.functional as F
from torch import tanh


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.a1 = nn.Conv2d(5, 16, kernel_size=3, padding=1)
        self.a2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
        self.a3 = nn.Conv2d(16, 32, kernel_size=3, stride=2)
        self.b1 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.b2 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.b3 = nn.Conv2d(32, 64, kernel_size=3, stride=2)
        self.c1 = nn.Conv2d(64, 64, kernel_size=2, padding=1)
        self.c2 = nn.Conv2d(64, 64, kernel_size=2, padding=1)
        self.c3 = nn.Conv2d(64, 128, kernel_size=2, stride=2)
        self.d1 = nn.Conv2d(128, 128, kernel_size=1)
        self.d2 = nn.Conv2d(128, 128, kernel_size=1)
        self.d3 = nn.Conv2d(128, 128, kernel_size=1)
        self.last = nn.Linear(128, 1)

    def forward(self, x):
        x = F.relu(self.a1(x))
        x = F.relu(self.a2(x))
        x = F.relu(self.a3(x))
        x = F.relu(self.b1(x))
        x = F.relu(self.b2(x))
        x = F.relu(self.b3(x))
        x = F.relu(self.c1(x))
        x = F.relu(self.c2(x))
        x = F.relu(self.c3(x))
        x = F.relu(self.d1(x))
        x = F.relu(self.d2(x))
        x = F.relu(self.d3(x))
        x = x.view(-1, 128)
        x = self.last(x)
        return tanh(x)


def get_inputs():
    return [torch.rand([4, 5, 64, 64])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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 = 80
    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 % 5
    y1 = yindex // 5
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 5 * x2 + 45 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 20
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 5
    y1 = yindex // 5
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 5 * x2 + 20480 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    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):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 32 * x2 + 288 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 32 * x2 + 288 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 256 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 256 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 57600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 73984
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 64
    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
    y0 = yindex % 128
    y1 = yindex // 128
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 8192 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 64 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 128 * x2 + 8192 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_tanh_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27) = args
    args.clear()
    assert_size_stride(primals_1, (16, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 5, 64, 64), (20480, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (64, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_17, (64,), (1,))
    assert_size_stride(primals_18, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (1, 128), (128, 1))
    assert_size_stride(primals_27, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 5, 3, 3), (45, 1, 15, 5), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(80, 9)](primals_1, buf0, 80, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 5, 64, 64), (20480, 1, 320, 5), torch
            .float32)
        triton_poi_fused_1[grid(20, 4096)](primals_3, buf1, 20, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((16, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_2[grid(256, 9)](primals_4, buf2, 256, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((32, 16, 3, 3), (144, 1, 48, 16), torch.
            float32)
        triton_poi_fused_3[grid(512, 9)](primals_6, buf3, 512, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((32, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_4[grid(1024, 9)](primals_8, buf4, 1024, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf5 = empty_strided_cuda((32, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_4[grid(1024, 9)](primals_10, buf5, 1024, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf6 = empty_strided_cuda((64, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_5[grid(2048, 9)](primals_12, buf6, 2048, 9, XBLOCK
            =16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_12
        buf7 = empty_strided_cuda((64, 64, 2, 2), (256, 1, 128, 64), torch.
            float32)
        triton_poi_fused_6[grid(4096, 4)](primals_14, buf7, 4096, 4, XBLOCK
            =4, YBLOCK=256, num_warps=4, num_stages=1)
        del primals_14
        buf8 = empty_strided_cuda((64, 64, 2, 2), (256, 1, 128, 64), torch.
            float32)
        triton_poi_fused_6[grid(4096, 4)](primals_16, buf8, 4096, 4, XBLOCK
            =4, YBLOCK=256, num_warps=4, num_stages=1)
        del primals_16
        buf9 = empty_strided_cuda((128, 64, 2, 2), (256, 1, 128, 64), torch
            .float32)
        triton_poi_fused_7[grid(8192, 4)](primals_18, buf9, 8192, 4, XBLOCK
            =4, YBLOCK=256, num_warps=4, num_stages=1)
        del primals_18
        buf10 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 1, 1024, 16))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_8[grid(262144)](buf11, primals_2,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf12 = extern_kernels.convolution(buf11, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 16, 64, 64), (65536, 1, 1024, 16))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_8[grid(262144)](buf13, primals_5,
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf14 = extern_kernels.convolution(buf13, buf3, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_9[grid(123008)](buf15, primals_7,
            123008, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf16 = extern_kernels.convolution(buf15, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_9[grid(123008)](buf17, primals_9,
            123008, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf18 = extern_kernels.convolution(buf17, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 32, 31, 31), (30752, 1, 992, 32))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_9[grid(123008)](buf19, primals_11,
            123008, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf20 = extern_kernels.convolution(buf19, buf6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 15, 15), (14400, 1, 960, 64))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_10[grid(57600)](buf21, primals_13,
            57600, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_13
        buf22 = extern_kernels.convolution(buf21, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 16, 16), (16384, 1, 1024, 64))
        buf23 = buf22
        del buf22
        triton_poi_fused_convolution_relu_11[grid(65536)](buf23, primals_15,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_15
        buf24 = extern_kernels.convolution(buf23, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 17, 17), (18496, 1, 1088, 64))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_12[grid(73984)](buf25, primals_17,
            73984, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf26 = extern_kernels.convolution(buf25, buf9, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_13[grid(32768)](buf27, primals_19,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf28 = extern_kernels.convolution(buf27, primals_20, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf29 = buf28
        del buf28
        triton_poi_fused_convolution_relu_13[grid(32768)](buf29, primals_21,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
        buf30 = extern_kernels.convolution(buf29, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_relu_13[grid(32768)](buf31, primals_23,
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_23
        buf32 = extern_kernels.convolution(buf31, primals_24, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf32, (4, 128, 8, 8), (8192, 1, 1024, 128))
        buf33 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
            float32)
        buf36 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_14[grid(512, 64)](
            buf32, primals_25, buf33, buf36, 512, 64, XBLOCK=64, YBLOCK=64,
            num_warps=8, num_stages=1)
        del buf32
        del primals_25
        buf34 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf33, (256, 128), (128, 1), 0
            ), reinterpret_tensor(primals_26, (128, 1), (1, 128), 0), out=buf34
            )
        buf35 = buf34
        del buf34
        triton_poi_fused_tanh_15[grid(256)](buf35, primals_27, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_27
    return (buf35, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
        buf9, primals_20, primals_22, primals_24, buf11, buf13, buf15,
        buf17, buf19, buf21, buf23, buf25, buf27, buf29, buf31,
        reinterpret_tensor(buf33, (256, 128), (128, 1), 0), buf35,
        primals_26, buf36)


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.a1 = nn.Conv2d(5, 16, kernel_size=3, padding=1)
        self.a2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
        self.a3 = nn.Conv2d(16, 32, kernel_size=3, stride=2)
        self.b1 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.b2 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.b3 = nn.Conv2d(32, 64, kernel_size=3, stride=2)
        self.c1 = nn.Conv2d(64, 64, kernel_size=2, padding=1)
        self.c2 = nn.Conv2d(64, 64, kernel_size=2, padding=1)
        self.c3 = nn.Conv2d(64, 128, kernel_size=2, stride=2)
        self.d1 = nn.Conv2d(128, 128, kernel_size=1)
        self.d2 = nn.Conv2d(128, 128, kernel_size=1)
        self.d3 = nn.Conv2d(128, 128, kernel_size=1)
        self.last = nn.Linear(128, 1)

    def forward(self, input_0):
        primals_1 = self.a1.weight
        primals_2 = self.a1.bias
        primals_4 = self.a2.weight
        primals_5 = self.a2.bias
        primals_6 = self.a3.weight
        primals_7 = self.a3.bias
        primals_8 = self.b1.weight
        primals_9 = self.b1.bias
        primals_10 = self.b2.weight
        primals_11 = self.b2.bias
        primals_12 = self.b3.weight
        primals_13 = self.b3.bias
        primals_14 = self.c1.weight
        primals_15 = self.c1.bias
        primals_16 = self.c2.weight
        primals_17 = self.c2.bias
        primals_18 = self.c3.weight
        primals_19 = self.c3.bias
        primals_20 = self.d1.weight
        primals_21 = self.d1.bias
        primals_22 = self.d2.weight
        primals_23 = self.d2.bias
        primals_24 = self.d3.weight
        primals_25 = self.d3.bias
        primals_26 = self.last.weight
        primals_27 = self.last.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27])
        return output[0]
