# 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_4/inductor_cache/3f/c3fgjsm5fivhanyf6hctuqrseqqhknvm25rlb6lb4ufuqtm6sl2o.py
# Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[67108864], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 67108864
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 262144) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j2/cj2yfcllcub24s3jrndzyfhfo3n5454orcp4wg64d6sqohwrhcso.py
# Topologically Sorted Source Nodes: [conv3d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[33554432], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 33554432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 65536) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dj/cdj6qmto5epqvflpaaiybspkn3xfw72ggcrflzaw56iz4n5foqwh.py
# Topologically Sorted Source Nodes: [conv3d_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_6, %primals_7, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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=[8388608], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8388608
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 8192) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/on/conqgiab2kbcuunkfjjmqbo2e2j6bbgh42wyjfma4dtqwmyho4bi.py
# Topologically Sorted Source Nodes: [conv3d_4], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d_4 => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_4, %primals_10, %primals_11, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 512
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ix/cixzv3wb2hq6qgzwsfnxqlxglig5jifthljzui66lp6hn3cyw2wy.py
# Topologically Sorted Source Nodes: [conv3d_6], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv3d_6 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_6, %primals_14, %primals_15, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 128) % 512
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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, primals_23 = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3, 3), (81, 27, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64, 64), (786432, 262144, 4096, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3, 3, 3), (1728, 27, 9, 3, 1))
    assert_size_stride(primals_5, (128, ), (1, ))
    assert_size_stride(primals_6, (256, 128, 3, 3, 3), (3456, 27, 9, 3, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (256, 256, 3, 3, 3), (6912, 27, 9, 3, 1))
    assert_size_stride(primals_9, (256, ), (1, ))
    assert_size_stride(primals_10, (512, 256, 3, 3, 3), (6912, 27, 9, 3, 1))
    assert_size_stride(primals_11, (512, ), (1, ))
    assert_size_stride(primals_12, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_13, (512, ), (1, ))
    assert_size_stride(primals_14, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_15, (512, ), (1, ))
    assert_size_stride(primals_16, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_17, (512, ), (1, ))
    assert_size_stride(primals_18, (4096, 8192), (8192, 1))
    assert_size_stride(primals_19, (4096, ), (1, ))
    assert_size_stride(primals_20, (4096, 4096), (4096, 1))
    assert_size_stride(primals_21, (4096, ), (1, ))
    assert_size_stride(primals_22, (4, 4096), (4096, 1))
    assert_size_stride(primals_23, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64, 64), (16777216, 262144, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 67108864, grid=grid(67108864), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool3d_with_indices]
        buf2 = torch.ops.aten.max_pool3d_with_indices.default(buf1, [1, 2, 2], [1, 2, 2])
        buf3 = buf2[0]
        buf4 = buf2[1]
        del buf2
        # Topologically Sorted Source Nodes: [conv3d_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 64, 32, 32), (8388608, 65536, 1024, 32, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [conv3d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf6, primals_5, 33554432, grid=grid(33554432), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool3d_with_indices]
        buf7 = torch.ops.aten.max_pool3d_with_indices.default(buf6, [2, 2, 2], [2, 2, 2])
        buf8 = buf7[0]
        buf9 = buf7[1]
        del buf7
        # Topologically Sorted Source Nodes: [conv3d_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf8, primals_6, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 32, 16, 16), (2097152, 8192, 256, 16, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [conv3d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf11, primals_7, 8388608, grid=grid(8388608), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv3d_3], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 32, 16, 16), (2097152, 8192, 256, 16, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv3d_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf13, primals_9, 8388608, grid=grid(8388608), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool3d_with_indices]
        buf14 = torch.ops.aten.max_pool3d_with_indices.default(buf13, [2, 2, 2], [2, 2, 2])
        buf15 = buf14[0]
        buf16 = buf14[1]
        del buf14
        # Topologically Sorted Source Nodes: [conv3d_4], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf15, primals_10, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 512, 16, 8, 8), (524288, 1024, 64, 8, 1))
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [conv3d_4], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf18, primals_11, 2097152, grid=grid(2097152), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv3d_5], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 512, 16, 8, 8), (524288, 1024, 64, 8, 1))
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [conv3d_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf20, primals_13, 2097152, grid=grid(2097152), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool3d_with_indices]
        buf21 = torch.ops.aten.max_pool3d_with_indices.default(buf20, [2, 2, 2], [2, 2, 2])
        buf22 = buf21[0]
        buf23 = buf21[1]
        del buf21
        # Topologically Sorted Source Nodes: [conv3d_6], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf22, primals_14, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 512, 8, 4, 4), (65536, 128, 16, 4, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv3d_6], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf25, primals_15, 262144, grid=grid(262144), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv3d_7], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_16, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 512, 8, 4, 4), (65536, 128, 16, 4, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [conv3d_7], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf27, primals_17, 262144, grid=grid(262144), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.max_pool3d_with_indices]
        buf28 = torch.ops.aten.max_pool3d_with_indices.default(buf27, [2, 2, 2], [2, 2, 2], [0, 1, 1])
        buf29 = buf28[0]
        buf30 = buf28[1]
        del buf28
        buf31 = empty_strided_cuda((9, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fc6_features], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf29, (9, 8192), (8192, 1), 0), reinterpret_tensor(primals_18, (8192, 4096), (1, 8192), 0), alpha=1, beta=1, out=buf31)
        del primals_19
        buf32 = empty_strided_cuda((9, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fc7_features], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_21, buf31, reinterpret_tensor(primals_20, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf32)
        del primals_21
        buf33 = empty_strided_cuda((9, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_23, buf32, reinterpret_tensor(primals_22, (4096, 4), (1, 4096), 0), alpha=1, beta=1, out=buf33)
        del primals_23
    return (buf33, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, buf1, buf3, buf4, buf6, buf8, buf9, buf11, buf13, buf15, buf16, buf18, buf20, buf22, buf23, buf25, buf27, buf30, reinterpret_tensor(buf29, (9, 8192), (8192, 1), 0), buf31, buf32, primals_22, primals_20, primals_18, )


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, 3, 3, 3, 3), (81, 27, 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, 3, 64, 64, 64), (786432, 262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, 64, 3, 3, 3), (1728, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((256, 128, 3, 3, 3), (3456, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 256, 3, 3, 3), (6912, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((512, 256, 3, 3, 3), (6912, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((512, 512, 3, 3, 3), (13824, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((512, 512, 3, 3, 3), (13824, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((512, 512, 3, 3, 3), (13824, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4096, 8192), (8192, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((4096, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((4, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn


class C3D(nn.Module):

    def __init__(self, num_classes):
        super(C3D, self).__init__()
        self.conv1a = nn.Conv3d(in_channels=3, out_channels=64, kernel_size
            =(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
        self.conv2a = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2),
            padding=(0, 1, 1))
        self.fc6 = nn.Linear(8192, 4096)
        self.fc7 = nn.Linear(4096, 4096)
        self.fc8 = nn.Linear(4096, num_classes)

    def forward(self, x):
        x = self.pool1(self.conv1a(x))
        x = self.pool2(self.conv2a(x))
        x = self.pool3(self.conv3b(self.conv3a(x)))
        x = self.pool4(self.conv4b(self.conv4a(x)))
        x = self.pool5(self.conv5b(self.conv5a(x)))
        x = x.view(-1, 8192)
        fc6_features = self.fc6(x)
        fc7_features = self.fc7(fc6_features)
        logits = self.fc8(fc7_features)
        return logits


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


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

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


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 262144 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 65536 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 8192 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 128 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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, primals_23
        ) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3, 3), (81, 27, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64, 64), (786432, 262144, 4096,
        64, 1))
    assert_size_stride(primals_4, (128, 64, 3, 3, 3), (1728, 27, 9, 3, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (256, 128, 3, 3, 3), (3456, 27, 9, 3, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (256, 256, 3, 3, 3), (6912, 27, 9, 3, 1))
    assert_size_stride(primals_9, (256,), (1,))
    assert_size_stride(primals_10, (512, 256, 3, 3, 3), (6912, 27, 9, 3, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_13, (512,), (1,))
    assert_size_stride(primals_14, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_15, (512,), (1,))
    assert_size_stride(primals_16, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (4096, 8192), (8192, 1))
    assert_size_stride(primals_19, (4096,), (1,))
    assert_size_stride(primals_20, (4096, 4096), (4096, 1))
    assert_size_stride(primals_21, (4096,), (1,))
    assert_size_stride(primals_22, (4, 4096), (4096, 1))
    assert_size_stride(primals_23, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64, 64), (16777216, 262144, 
            4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(67108864)](buf1, primals_2, 
            67108864, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = torch.ops.aten.max_pool3d_with_indices.default(buf1, [1, 2, 
            2], [1, 2, 2])
        buf3 = buf2[0]
        buf4 = buf2[1]
        del buf2
        buf5 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 64, 32, 32), (8388608, 65536, 
            1024, 32, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_1[grid(33554432)](buf6, primals_5, 
            33554432, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf7 = torch.ops.aten.max_pool3d_with_indices.default(buf6, [2, 2, 
            2], [2, 2, 2])
        buf8 = buf7[0]
        buf9 = buf7[1]
        del buf7
        buf10 = extern_kernels.convolution(buf8, primals_6, stride=(1, 1, 1
            ), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 256, 32, 16, 16), (2097152, 8192, 256,
            16, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_2[grid(8388608)](buf11, primals_7, 
            8388608, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1, 1, 
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 256, 32, 16, 16), (2097152, 8192, 256,
            16, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_2[grid(8388608)](buf13, primals_9, 
            8388608, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf14 = torch.ops.aten.max_pool3d_with_indices.default(buf13, [2, 2,
            2], [2, 2, 2])
        buf15 = buf14[0]
        buf16 = buf14[1]
        del buf14
        buf17 = extern_kernels.convolution(buf15, primals_10, stride=(1, 1,
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 512, 16, 8, 8), (524288, 1024, 64, 8, 1))
        buf18 = buf17
        del buf17
        triton_poi_fused_convolution_3[grid(2097152)](buf18, primals_11, 
            2097152, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1, 1,
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 512, 16, 8, 8), (524288, 1024, 64, 8, 1))
        buf20 = buf19
        del buf19
        triton_poi_fused_convolution_3[grid(2097152)](buf20, primals_13, 
            2097152, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_13
        buf21 = torch.ops.aten.max_pool3d_with_indices.default(buf20, [2, 2,
            2], [2, 2, 2])
        buf22 = buf21[0]
        buf23 = buf21[1]
        del buf21
        buf24 = extern_kernels.convolution(buf22, primals_14, stride=(1, 1,
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 512, 8, 4, 4), (65536, 128, 16, 4, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_4[grid(262144)](buf25, primals_15, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_15
        buf26 = extern_kernels.convolution(buf25, primals_16, stride=(1, 1,
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 512, 8, 4, 4), (65536, 128, 16, 4, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_4[grid(262144)](buf27, primals_17, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf28 = torch.ops.aten.max_pool3d_with_indices.default(buf27, [2, 2,
            2], [2, 2, 2], [0, 1, 1])
        buf29 = buf28[0]
        buf30 = buf28[1]
        del buf28
        buf31 = empty_strided_cuda((9, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf29, (9, 8192
            ), (8192, 1), 0), reinterpret_tensor(primals_18, (8192, 4096),
            (1, 8192), 0), alpha=1, beta=1, out=buf31)
        del primals_19
        buf32 = empty_strided_cuda((9, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_21, buf31, reinterpret_tensor(
            primals_20, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf32
            )
        del primals_21
        buf33 = empty_strided_cuda((9, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_23, buf32, reinterpret_tensor(
            primals_22, (4096, 4), (1, 4096), 0), alpha=1, beta=1, out=buf33)
        del primals_23
    return (buf33, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, buf1, buf3, buf4,
        buf6, buf8, buf9, buf11, buf13, buf15, buf16, buf18, buf20, buf22,
        buf23, buf25, buf27, buf30, reinterpret_tensor(buf29, (9, 8192), (
        8192, 1), 0), buf31, buf32, primals_22, primals_20, primals_18)


class C3DNew(nn.Module):

    def __init__(self, num_classes):
        super(C3DNew, self).__init__()
        self.conv1a = nn.Conv3d(in_channels=3, out_channels=64, kernel_size
            =(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
        self.conv2a = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
        self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1,
            1, 1))
        self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2),
            padding=(0, 1, 1))
        self.fc6 = nn.Linear(8192, 4096)
        self.fc7 = nn.Linear(4096, 4096)
        self.fc8 = nn.Linear(4096, num_classes)

    def forward(self, input_0):
        primals_1 = self.conv1a.weight
        primals_2 = self.conv1a.bias
        primals_4 = self.conv2a.weight
        primals_5 = self.conv2a.bias
        primals_6 = self.conv3a.weight
        primals_7 = self.conv3a.bias
        primals_8 = self.conv3b.weight
        primals_9 = self.conv3b.bias
        primals_10 = self.conv4a.weight
        primals_11 = self.conv4a.bias
        primals_12 = self.conv4b.weight
        primals_13 = self.conv4b.bias
        primals_14 = self.conv5a.weight
        primals_15 = self.conv5a.bias
        primals_16 = self.conv5b.weight
        primals_17 = self.conv5b.bias
        primals_18 = self.fc6.weight
        primals_19 = self.fc6.bias
        primals_20 = self.fc7.weight
        primals_21 = self.fc7.bias
        primals_22 = self.fc8.weight
        primals_23 = self.fc8.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23])
        return output[0]
