# 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/3r/c3rslv2exiiurkywxt4oxifqli4zlmoxh26mfj7b2xmxbacdj7n6.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 = 96
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (27*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/im/cimhbg2d5dyokflxdick6fp7bxrozyixlq7fktorhqhyhxxlqw27.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=[8192, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 8192
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 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/wa/cwaxgzlsyoq4nciniczgancxu2276femxorri42ynpeycltxtjyo.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=[131072, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 131072
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qg/cqg4z653mpzmif22rwtpmv42y4lbkkxhxjqguwoxl3wb6cn5fn7k.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=[262144, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 262144
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (512*x2) + (4608*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2y/c2yttjp523555saydyimerku3g5abcwcq5npinqqc2qrabybcqtu.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=[524288, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 524288
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (512*x2) + (4608*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/de/cdeln2tj32kp6x2644gzh2ys7hun75bqr36burzkblzjwgs4uqjx.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   x => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, 255.0), kwargs = {})
triton_poi_fused_div_5 = async_compile.triton('triton_poi_fused_div_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=[16, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_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_div_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tmp1 = 0.00392156862745098
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (y0 + (3*x2) + (12288*y1)), tmp2, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/l3/cl3q252wgi3hxvxd3zbecpu3422axihexl7ccuy2abyg7ik2fyna.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   out => convolution
#   out_1 => constant_pad_nd
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%div, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%convolution, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_6 = async_compile.triton('triton_poi_fused_constant_pad_nd_convolution_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[65536], 
    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_constant_pad_nd_convolution_6', '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_constant_pad_nd_convolution_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 52272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 198) % 66
    x1 = (xindex // 3) % 66
    x3 = (xindex // 13068)
    x4 = xindex % 198
    x0 = xindex % 3
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-195) + x4 + (192*x2) + (12288*x3)), tmp10 & xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp10, tmp13, tmp14)
    tl.store(out_ptr0 + (x6), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lg/clgtqhalpmnf7uokg33ha3vgwam42nbz7toy6svnopug43m7hqlp.py
# Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   out_2 => convolution_1
#   out_3 => relu
#   out_4 => constant_pad_nd_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_relu_7 = async_compile.triton('triton_poi_fused_constant_pad_nd_convolution_relu_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_convolution_relu_7', '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_constant_pad_nd_convolution_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 2112) % 66
    x1 = (xindex // 32) % 66
    x3 = (xindex // 139392)
    x4 = xindex % 2112
    x0 = xindex % 32
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-2080) + x4 + (2048*x2) + (131072*x3)), tmp10 & xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + (x6), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hm/chm5iqtfasmjkzseox33t2dndy7unxmt5p33p4snkvydoeglz3mz.py
# Topologically Sorted Source Nodes: [out_5, pool1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out_5 => convolution_2
#   pool1 => relu_1
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), 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=[4194304], 
    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 = 4194304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 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/jb/cjbfymhhk4q6semr2qdwcgrf5mffmc2jfkxk5av3nwbj3r5lm2tj.py
# Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   out_6 => getitem_1
# Graph fragment:
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_9 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_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_max_pool2d_with_indices_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = (xindex // 256) % 32
    x2 = (xindex // 8192)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (512*x1) + (32768*x2)), None)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + (512*x1) + (32768*x2)), None)
    tmp7 = tl.load(in_ptr0 + (16384 + x0 + (512*x1) + (32768*x2)), None)
    tmp12 = tl.load(in_ptr0 + (16640 + x0 + (512*x1) + (32768*x2)), None)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xg/cxgrawlzbv3ixwjtl5jz2b745e3rmxs6f6ez2grthc4tuba3po3l.py
# Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.max_pool2d_with_indices, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   out_6 => _low_memory_max_pool2d_with_offsets
#   out_7 => constant_pad_nd_2
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%getitem, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_10 = async_compile.triton('triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_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=[2097152], 
    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_constant_pad_nd_max_pool2d_with_indices_10', '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_constant_pad_nd_max_pool2d_with_indices_10(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1183744
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 8704) % 34
    x1 = (xindex // 256) % 34
    x0 = xindex % 256
    x3 = (xindex // 295936)
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-33280) + x0 + (512*x1) + (32768*x2) + (1048576*x3)), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr0 + ((-33024) + x0 + (512*x1) + (32768*x2) + (1048576*x3)), tmp10, other=0.0)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr0 + ((-16896) + x0 + (512*x1) + (32768*x2) + (1048576*x3)), tmp10, other=0.0)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.load(in_ptr0 + ((-16640) + x0 + (512*x1) + (32768*x2) + (1048576*x3)), tmp10, other=0.0)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tl.store(out_ptr0 + (x6), tmp19, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/65/c65xi35xkv3gngbv7ir3zt444ef2ucucl7sgmrx37a6c6cn63kbm.py
# Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   out_10 => constant_pad_nd_3
#   out_8 => convolution_3
#   out_9 => relu_2
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
#   %constant_pad_nd_3 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu_2, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_convolution_relu_11 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[4194304], 
    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_constant_pad_nd_convolution_relu_11', '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_constant_pad_nd_convolution_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2367488
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 17408) % 34
    x1 = (xindex // 512) % 34
    x3 = (xindex // 591872)
    x4 = xindex % 17408
    x0 = xindex % 512
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-16896) + x4 + (16384*x2) + (524288*x3)), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr1 + (x0), tmp10, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + (x6), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/k6/ck6glhgwq3r3erzhskbe5t27lp3hhialswo2amg7pwzftcf6cvhg.py
# Topologically Sorted Source Nodes: [out_11, pool2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out_11 => convolution_4
#   pool2 => relu_3
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_3, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), 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=[2097152], 
    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 = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/es/cesh2yb7gf6pzelxjhib4cmu37trrs4ynuuqrzt3losbflp5fos2.py
# Topologically Sorted Source Nodes: [out_12], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   out_12 => getitem_3
# Graph fragment:
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_13 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_13(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x1 = (xindex // 512) % 16
    x2 = (xindex // 8192)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (1024*x1) + (32768*x2)), None)
    tmp1 = tl.load(in_ptr0 + (512 + x0 + (1024*x1) + (32768*x2)), None)
    tmp7 = tl.load(in_ptr0 + (16384 + x0 + (1024*x1) + (32768*x2)), None)
    tmp12 = tl.load(in_ptr0 + (16896 + x0 + (1024*x1) + (32768*x2)), None)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/g3/cg3fxr4aqrrfoa72cdhibt6fhrqoayixfbrcvokbxjfiyfdccgjj.py
# Topologically Sorted Source Nodes: [out_12, out_13], Original ATen: [aten.max_pool2d_with_indices, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   out_12 => _low_memory_max_pool2d_with_offsets_1
#   out_13 => constant_pad_nd_4
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %constant_pad_nd_4 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%getitem_2, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14 = async_compile.triton('triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 663552
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 9216) % 18
    x1 = (xindex // 512) % 18
    x0 = xindex % 512
    x3 = (xindex // 165888)
    x6 = xindex
    tmp0 = (-1) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-33792) + x0 + (1024*x1) + (32768*x2) + (524288*x3)), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr0 + ((-33280) + x0 + (1024*x1) + (32768*x2) + (524288*x3)), tmp10, other=0.0)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr0 + ((-17408) + x0 + (1024*x1) + (32768*x2) + (524288*x3)), tmp10, other=0.0)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.load(in_ptr0 + ((-16896) + x0 + (1024*x1) + (32768*x2) + (524288*x3)), tmp10, other=0.0)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tl.store(out_ptr0 + (x6), tmp19, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lr/clrh4wthff4vzy7wxhk26tti2xbqzxqr7rehqns23z3kbk3q2zyj.py
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_14 => convolution_5
#   out_15 => relu_4
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_4, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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, 256], 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_15', '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_15(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 256
    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
    y0 = yindex % 1024
    y1 = (yindex // 1024)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (1024*x2) + (262144*y1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), None, 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 + (256*y3)), tmp4, xmask)
    tl.store(out_ptr1 + (y0 + (1024*x2) + (262144*y1)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/es/cesypybgzzoq6s6nox3idgljk44lfh2coyqrph4w37tep6bq5rsi.py
# Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_8 => convolution_3
#   out_9 => relu_2
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', '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_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gk/cgkbtscsdoi76kvfxxn32t6exrxsgg6w3gyc54eycdiamrhnbzeq.py
# Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_2 => convolution_1
#   out_3 => relu
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', '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_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_3, (3, ), (1, ))
    assert_size_stride(primals_4, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (256, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (512, ), (1, ))
    assert_size_stride(primals_10, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_11, (512, ), (1, ))
    assert_size_stride(primals_12, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_13, (1024, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((32, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_4, buf0, 96, 9, grid=grid(96, 9), stream=stream0)
        del primals_4
        buf1 = empty_strided_cuda((256, 32, 3, 3), (288, 1, 96, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_6, buf1, 8192, 9, grid=grid(8192, 9), stream=stream0)
        del primals_6
        buf2 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_8, buf2, 131072, 9, grid=grid(131072, 9), stream=stream0)
        del primals_8
        buf3 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_10, buf3, 262144, 9, grid=grid(262144, 9), stream=stream0)
        del primals_10
        buf4 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_12, buf4, 524288, 9, grid=grid(524288, 9), stream=stream0)
        del primals_12
        buf5 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
        triton_poi_fused_div_5.run(primals_1, buf5, 12, 4096, grid=grid(12, 4096), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 3, 64, 64), (12288, 1, 192, 3))
        buf7 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_6.run(buf6, primals_3, buf7, 52272, grid=grid(52272), stream=stream0)
        del buf6
        del primals_3
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, buf0, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 1, 2112, 32), torch.float32)
        # Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_relu_7.run(buf8, primals_5, buf9, 557568, grid=grid(557568), stream=stream0)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [out_5, pool1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf11, primals_7, 4194304, grid=grid(4194304), stream=stream0)
        del primals_7
        buf12 = empty_strided_cuda((4, 256, 32, 32), (262144, 1, 8192, 256), torch.int8)
        # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_9.run(buf11, buf12, 1048576, grid=grid(1048576), stream=stream0)
        buf13 = empty_strided_cuda((4, 256, 34, 34), (295936, 1, 8704, 256), torch.float32)
        # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.max_pool2d_with_indices, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_10.run(buf11, buf13, 1183744, grid=grid(1183744), stream=stream0)
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 512, 32, 32), (524288, 1, 16384, 512))
        buf15 = empty_strided_cuda((4, 512, 34, 34), (591872, 1, 17408, 512), torch.float32)
        # Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_convolution_relu_11.run(buf14, primals_9, buf15, 2367488, grid=grid(2367488), stream=stream0)
        # Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 512, 32, 32), (524288, 1, 16384, 512))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [out_11, pool2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf17, primals_11, 2097152, grid=grid(2097152), stream=stream0)
        del primals_11
        buf18 = empty_strided_cuda((4, 512, 16, 16), (131072, 1, 8192, 512), torch.int8)
        # Topologically Sorted Source Nodes: [out_12], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_13.run(buf17, buf18, 524288, grid=grid(524288), stream=stream0)
        buf19 = empty_strided_cuda((4, 512, 18, 18), (165888, 1, 9216, 512), torch.float32)
        # Topologically Sorted Source Nodes: [out_12, out_13], Original ATen: [aten.max_pool2d_with_indices, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14.run(buf17, buf19, 663552, grid=grid(663552), stream=stream0)
        # Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf21 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 1024), torch.bool)
        # Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_15.run(buf20, primals_13, buf21, buf22, 4096, 256, grid=grid(4096, 256), stream=stream0)
        del buf20
        del primals_13
        buf23 = empty_strided_cuda((4, 512, 32, 32), (524288, 1, 16384, 512), torch.bool)
        # Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_16.run(buf14, primals_9, buf23, 2097152, grid=grid(2097152), stream=stream0)
        del buf14
        del primals_9
        buf24 = empty_strided_cuda((4, 32, 64, 64), (131072, 1, 2048, 32), torch.bool)
        # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_17.run(buf8, primals_5, buf24, 524288, grid=grid(524288), stream=stream0)
        del buf8
        del primals_5
    return (buf21, primals_2, buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf22, buf23, buf24, )


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((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((3, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((1024, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((1024, ), (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])
    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
import torch
import torch.nn as nn


class encoder3(nn.Module):

    def __init__(self, W, v2):
        super(encoder3, self).__init__()
        self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
        self.reflecPad1 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv2 = nn.Conv2d(3, 32 if v2 else int(64 * W), 3, 1, 0)
        self.relu2 = nn.ReLU(inplace=True)
        self.reflecPad3 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv3 = nn.Conv2d(32 if v2 else int(64 * W), int(64 * W), 3, 1, 0)
        self.relu3 = nn.ReLU(inplace=True)
        self.maxPool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices
            =False)
        self.reflecPad4 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv4 = nn.Conv2d(int(64 * W), int(128 * W), 3, 1, 0)
        self.relu4 = nn.ReLU(inplace=True)
        self.reflecPad5 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv5 = nn.Conv2d(int(128 * W), int(128 * W), 3, 1, 0)
        self.relu5 = nn.ReLU(inplace=True)
        self.maxPool2 = nn.MaxPool2d(kernel_size=2, stride=2,
            return_indices=False)
        self.reflecPad6 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv6 = nn.Conv2d(int(128 * W), int(256 * W), 3, 1, 0)
        self.relu6 = nn.ReLU(inplace=True)

    def forward(self, x):
        x = x / 255.0
        out = self.conv1(x)
        out = self.reflecPad1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.reflecPad3(out)
        out = self.conv3(out)
        pool1 = self.relu3(out)
        out = self.maxPool(pool1)
        out = self.reflecPad4(out)
        out = self.conv4(out)
        out = self.relu4(out)
        out = self.reflecPad5(out)
        out = self.conv5(out)
        pool2 = self.relu5(out)
        out = self.maxPool2(pool2)
        out = self.reflecPad6(out)
        out = self.conv6(out)
        out = self.relu6(out)
        return out


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


def get_init_inputs():
    return [[], {'W': 4, 'v2': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch
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


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 96
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    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_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_div_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tmp1 = 0.00392156862745098
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp2, ymask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_6(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 52272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 198 % 66
    x1 = xindex // 3 % 66
    x3 = xindex // 13068
    x4 = xindex % 198
    x0 = xindex % 3
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-195 + x4 + 192 * x2 + 12288 * x3), tmp10 &
        xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + x0, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp10, tmp13, tmp14)
    tl.store(out_ptr0 + x6, tmp15, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_relu_7(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 2112 % 66
    x1 = xindex // 32 % 66
    x3 = xindex // 139392
    x4 = xindex % 2112
    x0 = xindex % 32
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-2080 + x4 + 2048 * x2 + 131072 * x3), tmp10 &
        xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + x0, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + x6, tmp17, 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 % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_9(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)
    x0 = xindex % 256
    x1 = xindex // 256 % 32
    x2 = xindex // 8192
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 32768 * x2), None)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 32768 * x2), None)
    tmp7 = tl.load(in_ptr0 + (16384 + x0 + 512 * x1 + 32768 * x2), None)
    tmp12 = tl.load(in_ptr0 + (16640 + x0 + 512 * x1 + 32768 * x2), None)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_10(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 // 8704 % 34
    x1 = xindex // 256 % 34
    x0 = xindex % 256
    x3 = xindex // 295936
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-33280 + x0 + 512 * x1 + 32768 * x2 + 
        1048576 * x3), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr0 + (-33024 + x0 + 512 * x1 + 32768 * x2 + 
        1048576 * x3), tmp10, other=0.0)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr0 + (-16896 + x0 + 512 * x1 + 32768 * x2 + 
        1048576 * x3), tmp10, other=0.0)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.load(in_ptr0 + (-16640 + x0 + 512 * x1 + 32768 * x2 + 
        1048576 * x3), tmp10, other=0.0)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tl.store(out_ptr0 + x6, tmp19, None)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_relu_11(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 17408 % 34
    x1 = xindex // 512 % 34
    x3 = xindex // 591872
    x4 = xindex % 17408
    x0 = xindex % 512
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-16896 + x4 + 16384 * x2 + 524288 * x3),
        tmp10, other=0.0)
    tmp12 = tl.load(in_ptr1 + x0, tmp10, eviction_policy='evict_last',
        other=0.0)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tl.store(out_ptr0 + x6, tmp17, None)


@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_13(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)
    x0 = xindex % 512
    x1 = xindex // 512 % 16
    x2 = xindex // 8192
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 32768 * x2), None)
    tmp1 = tl.load(in_ptr0 + (512 + x0 + 1024 * x1 + 32768 * x2), None)
    tmp7 = tl.load(in_ptr0 + (16384 + x0 + 1024 * x1 + 32768 * x2), None)
    tmp12 = tl.load(in_ptr0 + (16896 + x0 + 1024 * x1 + 32768 * x2), None)
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14(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 // 9216 % 18
    x1 = xindex // 512 % 18
    x0 = xindex % 512
    x3 = xindex // 165888
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-33792 + x0 + 1024 * x1 + 32768 * x2 + 
        524288 * x3), tmp10, other=0.0)
    tmp12 = tl.load(in_ptr0 + (-33280 + x0 + 1024 * x1 + 32768 * x2 + 
        524288 * x3), tmp10, other=0.0)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr0 + (-17408 + x0 + 1024 * x1 + 32768 * x2 + 
        524288 * x3), tmp10, other=0.0)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.load(in_ptr0 + (-16896 + x0 + 1024 * x1 + 32768 * x2 + 
        524288 * x3), tmp10, other=0.0)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tl.store(out_ptr0 + x6, tmp19, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    xnumel = 256
    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
    y0 = yindex % 1024
    y1 = yindex // 1024
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 1024 * x2 + 262144 * y1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, None, 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 + 256 * y3), tmp4, xmask)
    tl.store(out_ptr1 + (y0 + 1024 * x2 + 262144 * y1), tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (3, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_3, (3,), (1,))
    assert_size_stride(primals_4, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (256, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (512, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (1024, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_13, (1024,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((32, 3, 3, 3), (27, 1, 9, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(96, 9)](primals_4, buf0, 96, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((256, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_1[grid(8192, 9)](primals_6, buf1, 8192, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_2[grid(131072, 9)](primals_8, buf2, 131072, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf3 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_3[grid(262144, 9)](primals_10, buf3, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((1024, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_4[grid(524288, 9)](primals_12, buf4, 524288, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_12
        buf5 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_div_5[grid(12, 4096)](primals_1, buf5, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf6 = extern_kernels.convolution(buf5, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 3, 64, 64), (12288, 1, 192, 3))
        buf7 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch
            .float32)
        triton_poi_fused_constant_pad_nd_convolution_6[grid(52272)](buf6,
            primals_3, buf7, 52272, XBLOCK=512, num_warps=4, num_stages=1)
        del buf6
        del primals_3
        buf8 = extern_kernels.convolution(buf7, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 1, 2048, 32))
        buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 1, 2112, 32),
            torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_relu_7[grid(557568)](buf8,
            primals_5, buf9, 557568, XBLOCK=512, num_warps=8, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_8[grid(4194304)](buf11, primals_7,
            4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((4, 256, 32, 32), (262144, 1, 8192, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_9[grid(1048576)](buf11,
            buf12, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
        buf13 = empty_strided_cuda((4, 256, 34, 34), (295936, 1, 8704, 256),
            torch.float32)
        triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_10[grid(
            1183744)](buf11, buf13, 1183744, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 512, 32, 32), (524288, 1, 16384, 512))
        buf15 = empty_strided_cuda((4, 512, 34, 34), (591872, 1, 17408, 512
            ), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_relu_11[grid(2367488)](
            buf14, primals_9, buf15, 2367488, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf16 = extern_kernels.convolution(buf15, buf3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 512, 32, 32), (524288, 1, 16384, 512))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_12[grid(2097152)](buf17,
            primals_11, 2097152, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf18 = empty_strided_cuda((4, 512, 16, 16), (131072, 1, 8192, 512),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_13[grid(524288)](buf17,
            buf18, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        buf19 = empty_strided_cuda((4, 512, 18, 18), (165888, 1, 9216, 512),
            torch.float32)
        triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_14[grid(
            663552)](buf17, buf19, 663552, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf21 = empty_strided_cuda((4, 1024, 16, 16), (262144, 256, 16, 1),
            torch.float32)
        buf22 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_15[grid(4096, 256)
            ](buf20, primals_13, buf21, buf22, 4096, 256, XBLOCK=32, YBLOCK
            =32, num_warps=4, num_stages=1)
        del buf20
        del primals_13
        buf23 = empty_strided_cuda((4, 512, 32, 32), (524288, 1, 16384, 512
            ), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_16[grid(2097152)](
            buf14, primals_9, buf23, 2097152, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf14
        del primals_9
        buf24 = empty_strided_cuda((4, 32, 64, 64), (131072, 1, 2048, 32),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_17[grid(524288)](
            buf8, primals_5, buf24, 524288, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf8
        del primals_5
    return (buf21, primals_2, buf0, buf1, buf2, buf3, buf4, buf5, buf7,
        buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf22, buf23,
        buf24)


class encoder3New(nn.Module):

    def __init__(self, W, v2):
        super(encoder3New, self).__init__()
        self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
        self.reflecPad1 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv2 = nn.Conv2d(3, 32 if v2 else int(64 * W), 3, 1, 0)
        self.relu2 = nn.ReLU(inplace=True)
        self.reflecPad3 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv3 = nn.Conv2d(32 if v2 else int(64 * W), int(64 * W), 3, 1, 0)
        self.relu3 = nn.ReLU(inplace=True)
        self.maxPool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices
            =False)
        self.reflecPad4 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv4 = nn.Conv2d(int(64 * W), int(128 * W), 3, 1, 0)
        self.relu4 = nn.ReLU(inplace=True)
        self.reflecPad5 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv5 = nn.Conv2d(int(128 * W), int(128 * W), 3, 1, 0)
        self.relu5 = nn.ReLU(inplace=True)
        self.maxPool2 = nn.MaxPool2d(kernel_size=2, stride=2,
            return_indices=False)
        self.reflecPad6 = nn.ZeroPad2d((1, 1, 1, 1))
        self.conv6 = nn.Conv2d(int(128 * W), int(256 * W), 3, 1, 0)
        self.relu6 = nn.ReLU(inplace=True)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_12 = self.conv6.weight
        primals_13 = self.conv6.bias
        primals_1 = 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])
        return output[0]
