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

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


# kernel path: runs/run_shard_7/inductor_cache/2q/c2qsph7yuvd4qrjdx7qhitc2tkim3pjng4rqgufiypesenwycnhv.py
# Topologically Sorted Source Nodes: [conv3d, h], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d => convolution
#   h => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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_relu_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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/eu/ceuil3ionjy5idiy2xjrrzjxcgrg2fvxv4ss4ir6tq6ujzy4reaj.py
# Topologically Sorted Source Nodes: [conv3d_1, h_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d_1 => convolution_1
#   h_2 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = 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 = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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_relu_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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nn/cnn7hqqmtvi4uqysugefq7m5ihityrhrm67vilpt5jxdaxwfcfpi.py
# Topologically Sorted Source Nodes: [conv3d_2, h_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d_2 => convolution_2
#   h_4 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = 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 = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_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_relu_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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ak/cako5owywlvsqzbhtqdpp4mnk6y5rgiwicwor2sud27i4p2v7h4b.py
# Topologically Sorted Source Nodes: [conv3d_4, h_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d_4 => convolution_4
#   h_7 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = 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 = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_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_relu_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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2m/c2mlcrdzpklk4uu7woynsfes6p6r456zqy544wvawz5m7bdynwhk.py
# Topologically Sorted Source Nodes: [conv3d_6, h_10], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d_6 => convolution_6
#   h_10 => relu_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = 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 = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_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_relu_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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7f/c7fsm76ekzbme3x3pcwji4sqrwbua2jjzyzs4f75qcr3ojbocfpn.py
# Topologically Sorted Source Nodes: [h_14], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   h_14 => relu_8
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_19), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_5 = async_compile.triton('triton_poi_fused_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_5', '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_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 36864
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4096
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ci/ccicdznor3tpnzoknsctuog6zgbstipaqm2gru3jze4dyyxeyem7.py
# Topologically Sorted Source Nodes: [h_16], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   h_16 => relu_9
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_21), kwargs = {})
#   %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_6 = async_compile.triton('triton_poi_fused_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_6', '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_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qv/cqv5xnzrwboufcidxxxwfv4m7lbtkceulydrckonqpx3wdgp3j5z.py
# Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   probs => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm_2, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_7 = async_compile.triton('triton_poi_fused__softmax_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=[16], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (5*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (5*x0)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp1 - tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp8
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/up/cupmrnkfsawgfxio5beefkxtolreysogyueupq7s3otlegmkpytq.py
# Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   probs => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm_2, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_8 = async_compile.triton('triton_poi_fused__softmax_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=[64], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_8(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 45
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 5)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23 = 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, (1024, 4096), (4096, 1))
    assert_size_stride(primals_21, (1024, ), (1, ))
    assert_size_stride(primals_22, (5, 1024), (1024, 1))
    assert_size_stride(primals_23, (5, ), (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, h], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 67108864, grid=grid(67108864), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [h_1], 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, h_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf6, primals_5, 33554432, grid=grid(33554432), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [h_3], 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, h_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_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, h_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf13, primals_9, 8388608, grid=grid(8388608), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [h_6], 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, h_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_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, h_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf20, primals_13, 2097152, grid=grid(2097152), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [h_9], 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, h_10], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_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, h_11], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf27, primals_17, 262144, grid=grid(262144), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [h_12], 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: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf29, (9, 8192), (8192, 1), 0), reinterpret_tensor(primals_18, (8192, 4096), (1, 8192), 0), out=buf31)
        buf32 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [h_14], Original ATen: [aten.relu]
        triton_poi_fused_relu_5.run(buf32, primals_19, 36864, grid=grid(36864), stream=stream0)
        del primals_19
        buf33 = empty_strided_cuda((9, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf32, reinterpret_tensor(primals_20, (4096, 1024), (1, 4096), 0), out=buf33)
        buf34 = buf33; del buf33  # reuse
        # Topologically Sorted Source Nodes: [h_16], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf34, primals_21, 9216, grid=grid(9216), stream=stream0)
        del primals_21
        buf35 = empty_strided_cuda((9, 5), (5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_23, buf34, reinterpret_tensor(primals_22, (1024, 5), (1, 1024), 0), alpha=1, beta=1, out=buf35)
        del primals_23
        buf36 = empty_strided_cuda((9, 1), (1, 9), torch.float32)
        buf37 = empty_strided_cuda((9, 1), (1, 9), torch.float32)
        # Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_7.run(buf35, buf36, buf37, 9, grid=grid(9), stream=stream0)
        buf38 = buf35; del buf35  # reuse
        # Topologically Sorted Source Nodes: [probs], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_8.run(buf38, buf36, buf37, 45, grid=grid(45), stream=stream0)
        del buf36
        del buf37
    return (buf38, 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), buf32, buf34, buf38, 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((1024, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((5, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((5, ), (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
import torch.nn


class C3D(nn.Module):
    """
    The C3D network as described in [1].
    """

    def __init__(self):
        super(C3D, self).__init__()
        self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
        self.conv2 = 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, 1024)
        self.fc8 = nn.Linear(1024, 5)
        self.dropout = nn.Dropout(p=0.5)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax()

    def forward(self, x):
        h = self.relu(self.conv1(x))
        h = self.pool1(h)
        h = self.relu(self.conv2(h))
        h = self.pool2(h)
        h = self.relu(self.conv3a(h))
        h = self.relu(self.conv3b(h))
        h = self.pool3(h)
        h = self.relu(self.conv4a(h))
        h = self.relu(self.conv4b(h))
        h = self.pool4(h)
        h = self.relu(self.conv5a(h))
        h = self.relu(self.conv5b(h))
        h = self.pool5(h)
        h = h.view(-1, 8192)
        h = self.relu(self.fc6(h))
        h = self.dropout(h)
        h = self.relu(self.fc7(h))
        h = self.dropout(h)
        logits = self.fc8(h)
        probs = self.softmax(logits)
        return probs


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.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_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4096
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 9
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp1 - tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp8
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_8(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 45
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 5
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22, primals_23
        ) = 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, (1024, 4096), (4096, 1))
    assert_size_stride(primals_21, (1024,), (1,))
    assert_size_stride(primals_22, (5, 1024), (1024, 1))
    assert_size_stride(primals_23, (5,), (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_relu_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_relu_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_relu_2[grid(8388608)](buf11, primals_7,
            8388608, XBLOCK=1024, num_warps=4, 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_relu_2[grid(8388608)](buf13, primals_9,
            8388608, XBLOCK=1024, num_warps=4, 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_relu_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_relu_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_relu_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_relu_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.mm(reinterpret_tensor(buf29, (9, 8192), (8192, 1), 0
            ), reinterpret_tensor(primals_18, (8192, 4096), (1, 8192), 0),
            out=buf31)
        buf32 = buf31
        del buf31
        triton_poi_fused_relu_5[grid(36864)](buf32, primals_19, 36864,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_19
        buf33 = empty_strided_cuda((9, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(buf32, reinterpret_tensor(primals_20, (4096, 1024
            ), (1, 4096), 0), out=buf33)
        buf34 = buf33
        del buf33
        triton_poi_fused_relu_6[grid(9216)](buf34, primals_21, 9216, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_21
        buf35 = empty_strided_cuda((9, 5), (5, 1), torch.float32)
        extern_kernels.addmm(primals_23, buf34, reinterpret_tensor(
            primals_22, (1024, 5), (1, 1024), 0), alpha=1, beta=1, out=buf35)
        del primals_23
        buf36 = empty_strided_cuda((9, 1), (1, 9), torch.float32)
        buf37 = empty_strided_cuda((9, 1), (1, 9), torch.float32)
        triton_poi_fused__softmax_7[grid(9)](buf35, buf36, buf37, 9, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf38 = buf35
        del buf35
        triton_poi_fused__softmax_8[grid(45)](buf38, buf36, buf37, 45,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf36
        del buf37
    return (buf38, 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), buf32, buf34, buf38, primals_22, primals_20, primals_18)


class C3DNew(nn.Module):
    """
    The C3D network as described in [1].
    """

    def __init__(self):
        super(C3DNew, self).__init__()
        self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
        self.conv2 = 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, 1024)
        self.fc8 = nn.Linear(1024, 5)
        self.dropout = nn.Dropout(p=0.5)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax()

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.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]
