# 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_8/inductor_cache/pn/cpng7gl7lqxvqafyqlu5mbr4lc7m2sgi4l5ulbiv46djlkgyencv.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=[4096, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 4096
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ne/cnepmjd66uu3laeexeusfxab3aayptiri2wp2knrgtgmx52tvzxj.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 % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ba/cbayuw2by4w6xwduhs5qdriinmydiep6bpw7fyi37s377up7lrcm.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=[16384, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 16384
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/yd/cydvmxsmzwizyj5fbgjnjeeo27as6zdlft5s5uj57ovvcxtlbfhh.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=[32768, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 32768
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (128*x2) + (1152*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/hc/chc4ngj4mkoo4satyzg4z43rbbkycqq4jvvvdlykndskywnfxqkl.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=[65536, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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 = 65536
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = (yindex // 256)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/oj/cojdm37x644fv2njcx25pzo6tgwz6razjhwfcj3mv5aktyzxz2sl.py
# Topologically Sorted Source Nodes: [conv2d, x, conv2d_7, x_10], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   conv2d_7 => convolution_7
#   x => relu
#   x_10 => relu_7
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [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,), kwargs = {})
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_20, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_5 = async_compile.triton('triton_poi_fused_convolution_relu_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=[256, 8192], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 4900
    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 % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2 + (4900*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + (4900*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(out_ptr0 + (y0 + (64*x2) + (313600*y1)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (64*x2) + (313600*y1)), tmp7, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ky/ckyzqwdorf6grkwznrs3lgysd4knemcpjnprz4nw3smmft7g3snd.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1, conv2d_8, x_11], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   conv2d_8 => convolution_8
#   x_1 => relu_1
#   x_11 => relu_8
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [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_1,), kwargs = {})
#   %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
triton_poi_fused_convolution_relu_6 = async_compile.triton('triton_poi_fused_convolution_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[2097152], 
    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_convolution_relu_6', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_out_ptr1, in_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
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(in_out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fp/cfpyh4esn2bm7b3omoddx2l47m53elic6hscwdb3tdmktzughn77.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 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_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = (xindex // 64) % 34
    x2 = (xindex // 2176)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (128*x1) + (8704*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + (128*x1) + (8704*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (4352 + x0 + (128*x1) + (8704*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (4416 + x0 + (128*x1) + (8704*x2)), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/dm/cdms7xb2a7fjcmjtpfd5mlalweaqrgt6ztdvcsmk3fsaqczecgrx.py
# Topologically Sorted Source Nodes: [conv2d_2, x_3, conv2d_9, x_13], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   conv2d_9 => convolution_9
#   x_13 => relu_9
#   x_3 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [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_2,), kwargs = {})
#   %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), 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=[524288], 
    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_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_out_ptr1, in_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 % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(in_out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/em/cem5dhljbghteql5krusozkvxh6356iabhmkk63jpf2lidhc2daf.py
# Topologically Sorted Source Nodes: [conv2d_3, x_4, conv2d_10, x_14], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_10
#   conv2d_3 => convolution_3
#   x_14 => relu_10
#   x_4 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [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_3,), kwargs = {})
#   %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
triton_poi_fused_convolution_relu_9 = async_compile.triton('triton_poi_fused_convolution_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    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_convolution_relu_9', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 460800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(in_out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ov/covh4h5pbfthppfx7dl4aqot7xjrfuqhzavjgr5gcjaprird4dts.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_5 => getitem_2, getitem_3
# Graph fragment:
#   %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_10 = async_compile.triton('triton_poi_fused_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_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_max_pool2d_with_indices_10(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 115200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = (xindex // 128) % 15
    x2 = (xindex // 1920)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x1) + (7680*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + (256*x1) + (7680*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (3840 + x0 + (256*x1) + (7680*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (3968 + x0 + (256*x1) + (7680*x2)), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ps/cpskr7g67275egkzieaa6bx7lqsqx4wadlw234lkdag6f6hjow4w.py
# Topologically Sorted Source Nodes: [conv2d_4, x_6, conv2d_11, x_16], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_11 => convolution_11
#   conv2d_4 => convolution_4
#   x_16 => relu_11
#   x_6 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_10, %primals_11, [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_4,), kwargs = {})
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_8, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
triton_poi_fused_convolution_relu_11 = async_compile.triton('triton_poi_fused_convolution_relu_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    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_convolution_relu_11', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 173056
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/w3/cw3d3hwarkyjldwj4wgb6di24kvwmkkppv2flr2rpgj2rz3h33g2.py
# Topologically Sorted Source Nodes: [conv2d_5, x_7, conv2d_12, x_17], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_12 => convolution_12
#   conv2d_5 => convolution_5
#   x_17 => relu_12
#   x_7 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_11, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {})
triton_poi_fused_convolution_relu_12 = async_compile.triton('triton_poi_fused_convolution_relu_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_convolution_relu_12', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 123904
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/5q/c5qohqndt5jdi75bwvqwey6q5lv5h6qkn7yw326inr7ozj37qjan.py
# Topologically Sorted Source Nodes: [conv2d_6, x_8, conv2d_13, x_18], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_13 => convolution_13
#   conv2d_6 => convolution_6
#   x_18 => relu_13
#   x_8 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
#   %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_12, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_13 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {})
triton_poi_fused_convolution_relu_13 = async_compile.triton('triton_poi_fused_convolution_relu_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[131072], 
    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_convolution_relu_13', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/l3/cl3lthjgrzycrltmaiqfct2vpdlky2p5arooxwufr7uzrphcni5l.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_9 => _low_memory_max_pool2d_with_offsets_2, getitem_5
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_6, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_14 = async_compile.triton('triton_poi_fused_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=[64, 256], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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_max_pool2d_with_indices_14(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 256
    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
    x3 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4) % 4
    y2 = (yindex // 16)
    y4 = yindex
    y5 = yindex % 16
    tmp0 = tl.load(in_ptr0 + (x3 + (512*y0) + (4608*y1) + (20736*y2)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (256 + x3 + (512*y0) + (4608*y1) + (20736*y2)), xmask & ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2304 + x3 + (512*y0) + (4608*y1) + (20736*y2)), xmask & ymask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2560 + x3 + (512*y0) + (4608*y1) + (20736*y2)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1, 1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1, 1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x3 + (256*y4)), tmp15, xmask & ymask)
    tl.store(out_ptr1 + (y5 + (16*x3) + (4096*y2)), tmp16, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fk/cfk7e2b2oanx36so4fi3xffqgsvkfxcyjm76o55vild34avlxrap.py
# Topologically Sorted Source Nodes: [x1_2, x2_2, sub, x_20], Original ATen: [aten.sigmoid, aten.sub, aten.abs]
# Source node to ATen node mapping:
#   sub => sub
#   x1_2 => sigmoid
#   x2_2 => sigmoid_1
#   x_20 => abs_1
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm_1,), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm_3,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %sigmoid_1), kwargs = {})
#   %abs_1 : [num_users=2] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
triton_poi_fused_abs_sigmoid_sub_15 = async_compile.triton('triton_poi_fused_abs_sigmoid_sub_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_abs_sigmoid_sub_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_abs_sigmoid_sub_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp2 = tl.load(in_ptr1 + (x0), None)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tl.store(out_ptr0 + (x0), tmp5, 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, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 72, 72), (5184, 5184, 72, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256, ), (1, ))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256, ), (1, ))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256, ), (1, ))
    assert_size_stride(primals_16, (4096, 16384), (16384, 1))
    assert_size_stride(primals_17, (4096, ), (1, ))
    assert_size_stride(primals_18, (4096, 4096), (4096, 1))
    assert_size_stride(primals_19, (4096, ), (1, ))
    assert_size_stride(primals_20, (4, 1, 72, 72), (5184, 5184, 72, 1))
    assert_size_stride(primals_21, (1, 4096), (4096, 1))
    assert_size_stride(primals_22, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_4, buf0, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_4
        buf1 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), 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((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(primals_8, buf2, 16384, 9, grid=grid(16384, 9), stream=stream0)
        del primals_8
        buf3 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(primals_10, buf3, 32768, 9, grid=grid(32768, 9), stream=stream0)
        del primals_10
        buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_12, buf4, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_12
        buf5 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(primals_14, buf5, 65536, 9, grid=grid(65536, 9), stream=stream0)
        del primals_14
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 70, 70), (313600, 4900, 70, 1))
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(primals_20, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 64, 70, 70), (313600, 4900, 70, 1))
        buf7 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64), torch.float32)
        buf29 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, x, conv2d_7, x_10], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf6, primals_2, buf28, buf7, buf29, 256, 4900, grid=grid(256, 4900), stream=stream0)
        del buf28
        del buf6
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], 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, 64, 68, 68), (295936, 1, 4352, 64))
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf29, buf0, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 68, 68), (295936, 1, 4352, 64))
        buf9 = buf8; del buf8  # reuse
        buf31 = buf30; del buf30  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x_1, conv2d_8, x_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_6.run(buf9, buf31, primals_5, 1183744, grid=grid(1183744), stream=stream0)
        del primals_5
        buf10 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32)
        buf11 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.int8)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf9, buf10, buf11, 295936, grid=grid(295936), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf32 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32)
        buf33 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.int8)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_7.run(buf31, buf32, buf33, 295936, grid=grid(295936), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf34 = extern_kernels.convolution(buf32, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf13 = buf12; del buf12  # reuse
        buf35 = buf34; del buf34  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, x_3, conv2d_9, x_13], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_8.run(buf13, buf35, primals_7, 524288, grid=grid(524288), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], 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, 128, 30, 30), (115200, 1, 3840, 128))
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf36 = extern_kernels.convolution(buf35, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 128, 30, 30), (115200, 1, 3840, 128))
        buf15 = buf14; del buf14  # reuse
        buf37 = buf36; del buf36  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, x_4, conv2d_10, x_14], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_9.run(buf15, buf37, primals_9, 460800, grid=grid(460800), stream=stream0)
        del primals_9
        buf16 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128), torch.float32)
        buf17 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128), torch.int8)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_10.run(buf15, buf16, buf17, 115200, grid=grid(115200), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf16, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 256, 13, 13), (43264, 1, 3328, 256))
        buf38 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128), torch.float32)
        buf39 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128), torch.int8)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_10.run(buf37, buf38, buf39, 115200, grid=grid(115200), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution]
        buf40 = extern_kernels.convolution(buf38, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 13, 13), (43264, 1, 3328, 256))
        buf19 = buf18; del buf18  # reuse
        buf41 = buf40; del buf40  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, x_6, conv2d_11, x_16], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_11.run(buf19, buf41, primals_11, 173056, grid=grid(173056), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], 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, 256, 11, 11), (30976, 1, 2816, 256))
        # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution]
        buf42 = extern_kernels.convolution(buf41, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 11, 11), (30976, 1, 2816, 256))
        buf21 = buf20; del buf20  # reuse
        buf43 = buf42; del buf42  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, x_7, conv2d_12, x_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_12.run(buf21, buf43, primals_13, 123904, grid=grid(123904), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 256, 9, 9), (20736, 1, 2304, 256))
        # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf43, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 256, 9, 9), (20736, 1, 2304, 256))
        buf23 = buf22; del buf22  # reuse
        buf45 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, x_8, conv2d_13, x_18], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_13.run(buf23, buf45, primals_15, 82944, grid=grid(82944), stream=stream0)
        del primals_15
        buf24 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.int8)
        buf25 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_14.run(buf23, buf24, buf25, 64, 256, grid=grid(64, 256), stream=stream0)
        buf26 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf25, (1, 16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096), (1, 16384), 0), alpha=1, beta=1, out=buf26)
        buf27 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_19, buf26, reinterpret_tensor(primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf27)
        buf46 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.int8)
        buf47 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_14.run(buf45, buf46, buf47, 64, 256, grid=grid(64, 256), stream=stream0)
        buf48 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x2_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf47, (1, 16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096), (1, 16384), 0), alpha=1, beta=1, out=buf48)
        del primals_17
        buf49 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_19, buf48, reinterpret_tensor(primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf49)
        del primals_19
        buf50 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1_2, x2_2, sub, x_20], Original ATen: [aten.sigmoid, aten.sub, aten.abs]
        triton_poi_fused_abs_sigmoid_sub_15.run(buf27, buf49, buf50, 4096, grid=grid(4096), stream=stream0)
        buf52 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_22, buf50, reinterpret_tensor(primals_21, (4096, 1), (1, 4096), 0), alpha=1, beta=1, out=buf52)
        del primals_22
    return (buf52, primals_1, primals_3, buf0, buf1, buf2, buf3, buf4, buf5, primals_20, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17, buf19, buf21, buf23, buf24, reinterpret_tensor(buf25, (1, 16384), (16384, 1), 0), buf26, buf27, buf29, buf31, buf32, buf33, buf35, buf37, buf38, buf39, buf41, buf43, buf45, buf46, reinterpret_tensor(buf47, (1, 16384), (16384, 1), 0), buf48, buf49, buf50, primals_21, primals_18, primals_16, )


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((64, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 72, 72), (5184, 5184, 72, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((4096, 16384), (16384, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4096, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((4, 1, 72, 72), (5184, 5184, 72, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((1, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


class VGGSiameseNet(nn.Module):

    def __init__(self):
        super(VGGSiameseNet, self).__init__()
        self.conv11 = nn.Conv2d(1, 64, 3)
        self.conv12 = nn.Conv2d(64, 64, 3)
        self.conv21 = nn.Conv2d(64, 128, 3)
        self.conv22 = nn.Conv2d(128, 128, 3)
        self.conv31 = nn.Conv2d(128, 256, 3)
        self.conv32 = nn.Conv2d(256, 256, 3)
        self.conv33 = nn.Conv2d(256, 256, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(256 * 8 * 8, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fcOut = nn.Linear(4096, 1)
        self.sigmoid = nn.Sigmoid()

    def convs(self, x):
        x = F.relu(self.conv11(x))
        x = F.relu(self.conv12(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv21(x))
        x = F.relu(self.conv22(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv31(x))
        x = F.relu(self.conv32(x))
        x = F.relu(self.conv33(x))
        x = F.max_pool2d(x, (2, 2))
        return x

    def forward(self, x1, x2):
        x1 = self.convs(x1)
        x1 = x1.view(-1, 256 * 8 * 8)
        x1 = self.fc1(x1)
        x1 = self.sigmoid(self.fc2(x1))
        x2 = self.convs(x2)
        x2 = x2.view(-1, 256 * 8 * 8)
        x2 = self.fc1(x2)
        x2 = self.sigmoid(self.fc2(x2))
        x = torch.abs(x1 - x2)
        x = self.fcOut(x)
        return x


def get_inputs():
    return [torch.rand([4, 1, 72, 72]), torch.rand([4, 1, 72, 72])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_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 % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_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 % 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_convolution_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 4900
    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 % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4900 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + 4900 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(out_ptr0 + (y0 + 64 * x2 + 313600 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 64 * x2 + 313600 * y1), tmp7, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(in_out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64 % 34
    x2 = xindex // 2176
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8704 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8704 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (4352 + x0 + 128 * x1 + 8704 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (4416 + x0 + 128 * x1 + 8704 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(in_out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(in_out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_10(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 115200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 128
    x1 = xindex // 128 % 15
    x2 = xindex // 1920
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 7680 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 7680 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (3840 + x0 + 256 * x1 + 7680 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (3968 + x0 + 256 * x1 + 7680 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 173056
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 123904
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_14(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 256
    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
    x3 = xindex
    y0 = yindex % 4
    y1 = yindex // 4 % 4
    y2 = yindex // 16
    y4 = yindex
    y5 = yindex % 16
    tmp0 = tl.load(in_ptr0 + (x3 + 512 * y0 + 4608 * y1 + 20736 * y2), 
        xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (256 + x3 + 512 * y0 + 4608 * y1 + 20736 * y2),
        xmask & ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2304 + x3 + 512 * y0 + 4608 * y1 + 20736 * y2
        ), xmask & ymask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2560 + x3 + 512 * y0 + 4608 * y1 + 20736 *
        y2), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1, 1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1, 1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x3 + 256 * y4), tmp15, xmask & ymask)
    tl.store(out_ptr1 + (y5 + 16 * x3 + 4096 * y2), tmp16, xmask & ymask)


@triton.jit
def triton_poi_fused_abs_sigmoid_sub_15(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp2 = tl.load(in_ptr1 + x0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tl.store(out_ptr0 + x0, tmp5, 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, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22) = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 72, 72), (5184, 5184, 72, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (4096, 16384), (16384, 1))
    assert_size_stride(primals_17, (4096,), (1,))
    assert_size_stride(primals_18, (4096, 4096), (4096, 1))
    assert_size_stride(primals_19, (4096,), (1,))
    assert_size_stride(primals_20, (4, 1, 72, 72), (5184, 5184, 72, 1))
    assert_size_stride(primals_21, (1, 4096), (4096, 1))
    assert_size_stride(primals_22, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(4096, 9)](primals_4, buf0, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), 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((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_2[grid(16384, 9)](primals_8, buf2, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf3 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_3[grid(32768, 9)](primals_10, buf3, 32768, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_4[grid(65536, 9)](primals_12, buf4, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_12
        buf5 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_4[grid(65536, 9)](primals_14, buf5, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf6 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 70, 70), (313600, 4900, 70, 1))
        buf28 = extern_kernels.convolution(primals_20, primals_1, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 64, 70, 70), (313600, 4900, 70, 1))
        buf7 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64),
            torch.float32)
        buf29 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64),
            torch.float32)
        triton_poi_fused_convolution_relu_5[grid(256, 4900)](buf6,
            primals_2, buf28, buf7, buf29, 256, 4900, XBLOCK=16, YBLOCK=256,
            num_warps=8, num_stages=1)
        del buf28
        del buf6
        del primals_2
        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, 64, 68, 68), (295936, 1, 4352, 64))
        buf30 = extern_kernels.convolution(buf29, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf30, (4, 64, 68, 68), (295936, 1, 4352, 64))
        buf9 = buf8
        del buf8
        buf31 = buf30
        del buf30
        triton_poi_fused_convolution_relu_6[grid(1183744)](buf9, buf31,
            primals_5, 1183744, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf10 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
            torch.float32)
        buf11 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_7[grid(295936)](buf9,
            buf10, buf11, 295936, XBLOCK=512, num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf10, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf32 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
            torch.float32)
        buf33 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_7[grid(295936)](buf31,
            buf32, buf33, 295936, XBLOCK=512, num_warps=8, num_stages=1)
        buf34 = extern_kernels.convolution(buf32, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 128, 32, 32), (131072, 1, 4096, 128))
        buf13 = buf12
        del buf12
        buf35 = buf34
        del buf34
        triton_poi_fused_convolution_relu_8[grid(524288)](buf13, buf35,
            primals_7, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        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, 128, 30, 30), (115200, 1, 3840, 128))
        buf36 = extern_kernels.convolution(buf35, buf2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf36, (4, 128, 30, 30), (115200, 1, 3840, 128))
        buf15 = buf14
        del buf14
        buf37 = buf36
        del buf36
        triton_poi_fused_convolution_relu_9[grid(460800)](buf15, buf37,
            primals_9, 460800, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf16 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
            torch.float32)
        buf17 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_10[grid(115200)](buf15,
            buf16, buf17, 115200, XBLOCK=512, num_warps=8, num_stages=1)
        buf18 = extern_kernels.convolution(buf16, buf3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 256, 13, 13), (43264, 1, 3328, 256))
        buf38 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
            torch.float32)
        buf39 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_10[grid(115200)](buf37,
            buf38, buf39, 115200, XBLOCK=512, num_warps=8, num_stages=1)
        buf40 = extern_kernels.convolution(buf38, buf3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 13, 13), (43264, 1, 3328, 256))
        buf19 = buf18
        del buf18
        buf41 = buf40
        del buf40
        triton_poi_fused_convolution_relu_11[grid(173056)](buf19, buf41,
            primals_11, 173056, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        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, 256, 11, 11), (30976, 1, 2816, 256))
        buf42 = extern_kernels.convolution(buf41, buf4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf42, (4, 256, 11, 11), (30976, 1, 2816, 256))
        buf21 = buf20
        del buf20
        buf43 = buf42
        del buf42
        triton_poi_fused_convolution_relu_12[grid(123904)](buf21, buf43,
            primals_13, 123904, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_13
        buf22 = extern_kernels.convolution(buf21, buf5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 256, 9, 9), (20736, 1, 2304, 256))
        buf44 = extern_kernels.convolution(buf43, buf5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 256, 9, 9), (20736, 1, 2304, 256))
        buf23 = buf22
        del buf22
        buf45 = buf44
        del buf44
        triton_poi_fused_convolution_relu_13[grid(82944)](buf23, buf45,
            primals_15, 82944, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_15
        buf24 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
            torch.int8)
        buf25 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_14[grid(64, 256)](buf23,
            buf24, buf25, 64, 256, XBLOCK=4, YBLOCK=64, num_warps=4,
            num_stages=1)
        buf26 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf25, (1, 
            16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096
            ), (1, 16384), 0), alpha=1, beta=1, out=buf26)
        buf27 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_19, buf26, reinterpret_tensor(
            primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf27
            )
        buf46 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
            torch.int8)
        buf47 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_14[grid(64, 256)](buf45,
            buf46, buf47, 64, 256, XBLOCK=4, YBLOCK=64, num_warps=4,
            num_stages=1)
        buf48 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf47, (1, 
            16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096
            ), (1, 16384), 0), alpha=1, beta=1, out=buf48)
        del primals_17
        buf49 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_19, buf48, reinterpret_tensor(
            primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf49
            )
        del primals_19
        buf50 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
        triton_poi_fused_abs_sigmoid_sub_15[grid(4096)](buf27, buf49, buf50,
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        buf52 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_22, buf50, reinterpret_tensor(
            primals_21, (4096, 1), (1, 4096), 0), alpha=1, beta=1, out=buf52)
        del primals_22
    return (buf52, primals_1, primals_3, buf0, buf1, buf2, buf3, buf4, buf5,
        primals_20, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17,
        buf19, buf21, buf23, buf24, reinterpret_tensor(buf25, (1, 16384), (
        16384, 1), 0), buf26, buf27, buf29, buf31, buf32, buf33, buf35,
        buf37, buf38, buf39, buf41, buf43, buf45, buf46, reinterpret_tensor
        (buf47, (1, 16384), (16384, 1), 0), buf48, buf49, buf50, primals_21,
        primals_18, primals_16)


class VGGSiameseNetNew(nn.Module):

    def __init__(self):
        super(VGGSiameseNetNew, self).__init__()
        self.conv11 = nn.Conv2d(1, 64, 3)
        self.conv12 = nn.Conv2d(64, 64, 3)
        self.conv21 = nn.Conv2d(64, 128, 3)
        self.conv22 = nn.Conv2d(128, 128, 3)
        self.conv31 = nn.Conv2d(128, 256, 3)
        self.conv32 = nn.Conv2d(256, 256, 3)
        self.conv33 = nn.Conv2d(256, 256, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(256 * 8 * 8, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fcOut = nn.Linear(4096, 1)
        self.sigmoid = nn.Sigmoid()

    def convs(self, x):
        x = F.relu(self.conv11(x))
        x = F.relu(self.conv12(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv21(x))
        x = F.relu(self.conv22(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv31(x))
        x = F.relu(self.conv32(x))
        x = F.relu(self.conv33(x))
        x = F.max_pool2d(x, (2, 2))
        return x

    def forward(self, input_0, input_1):
        primals_1 = self.conv11.weight
        primals_2 = self.conv11.bias
        primals_4 = self.conv12.weight
        primals_5 = self.conv12.bias
        primals_6 = self.conv21.weight
        primals_7 = self.conv21.bias
        primals_8 = self.conv22.weight
        primals_9 = self.conv22.bias
        primals_10 = self.conv31.weight
        primals_11 = self.conv31.bias
        primals_12 = self.conv32.weight
        primals_13 = self.conv32.bias
        primals_14 = self.conv33.weight
        primals_15 = self.conv33.bias
        primals_16 = self.fc1.weight
        primals_17 = self.fc1.bias
        primals_18 = self.fc2.weight
        primals_19 = self.fc2.bias
        primals_21 = self.fcOut.weight
        primals_22 = self.fcOut.bias
        primals_3 = input_0
        primals_20 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22])
        return output[0]
