# 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_1/inductor_cache/e7/ce7pr5z43jbq2ziuqub4csjgjpmnceorclzg3sxvchjqcaguyxnh.py
# Topologically Sorted Source Nodes: [net], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   net => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1, 2, 2], [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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = (xindex // 4096) % 3
    x2 = (xindex // 12288) % 3
    x3 = (xindex // 36864)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4096*x2) + (12288*x1) + (36864*x3)), None)
    tl.store(out_ptr0 + (x4), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/77/c77rbxgqyczbzkgnjfjljh4cxbmv2tm7ovzbuecn5sauy42w232z.py
# Topologically Sorted Source Nodes: [net, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   net => convolution
#   relu => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1, 2, 2], [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_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 393216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 3072) % 32
    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_1/inductor_cache/4k/c4kmothpq6ult7xizjxu5usxyjzptlbpeygeiljfrmn36ynkxc4g.py
# Topologically Sorted Source Nodes: [net_1, relu_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   net_1 => convolution_1
#   relu_1 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2, 2], [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_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 512) % 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_1/inductor_cache/of/cof4mzgamiwwzjz5otq2rbtnkmhsev6rbjlqd4i6ut642zqlijov.py
# Topologically Sorted Source Nodes: [net_2, relu_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   net_2 => convolution_2
#   relu_2 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 2, 2], [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_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 128) % 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_1/inductor_cache/va/cva43mnmxwk2tredh4zjxuwj45dfxi66v4722ytaj4tjh57okg5d.py
# Topologically Sorted Source Nodes: [net_3, relu_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   net_3 => convolution_3
#   relu_3 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [2, 2, 2], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), 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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 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_1/inductor_cache/ae/caetzoeztrwhvyfwyvi7dsghfn37kawa4y3lpbtgnrxpepss2ykm.py
# Topologically Sorted Source Nodes: [net_4, relu_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   net_4 => convolution_4
#   relu_4 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [2, 2, 2], [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_5 = async_compile.triton('triton_poi_fused_convolution_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4) % 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_1/inductor_cache/df/cdf5cljf3qkejgpoap3ufyuokfbjarutn7tihtpkjn7lvggkt6do.py
# Topologically Sorted Source Nodes: [net_6, relu_5], Original ATen: [aten.mean, aten.relu]
# Source node to ATen node mapping:
#   net_6 => mean
#   relu_5 => relu_5
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [2]), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mean,), kwargs = {})
triton_poi_fused_mean_relu_6 = async_compile.triton('triton_poi_fused_mean_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_relu_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mean_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 / tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 3, 64, 64), (36864, 12288, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 3, 3, 3, 3), (81, 27, 9, 3, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (64, 32, 3, 3, 3), (864, 27, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3, 3), (1728, 27, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (256, 128, 3, 3, 3), (3456, 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, (128, 512), (512, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 3, 64, 64), (36864, 12288, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [net], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, 147456, grid=grid(147456), stream=stream0)
        # Topologically Sorted Source Nodes: [net], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 3, 32, 32), (98304, 3072, 1024, 32, 1))
        del buf0
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [net, relu], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf2, primals_3, 393216, grid=grid(393216), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [net_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 64, 2, 16, 16), (32768, 512, 256, 16, 1))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [net_1, relu_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf4, primals_5, 131072, grid=grid(131072), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [net_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 2, 2), 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, 2, 8, 8), (16384, 128, 64, 8, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [net_2, relu_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf6, primals_7, 65536, grid=grid(65536), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [net_3], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 1, 4, 4), (4096, 16, 16, 4, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [net_3, relu_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf8, primals_9, 16384, grid=grid(16384), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [net_4], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_10, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 1, 2, 2), (2048, 4, 4, 2, 1))
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [net_4, relu_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_5.run(buf10, primals_11, 8192, grid=grid(8192), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [net_5], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_12, stride=(2, 2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 512, 1, 1, 1), (512, 1, 1, 1, 1))
        buf12 = reinterpret_tensor(buf11, (4, 512), (512, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [net_6, relu_5], Original ATen: [aten.mean, aten.relu]
        triton_poi_fused_mean_relu_6.run(buf12, primals_13, 2048, grid=grid(2048), stream=stream0)
        del primals_13
        buf13 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_15, buf12, reinterpret_tensor(primals_14, (512, 128), (1, 512), 0), alpha=1, beta=1, out=buf13)
        del primals_15
    return (buf13, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, reinterpret_tensor(primals_1, (4, 3, 3, 64, 64), (36864, 4096, 12288, 64, 1), 0), buf2, buf4, buf6, buf8, buf10, buf12, primals_14, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 3, 3, 64, 64), (36864, 12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 3, 3, 3, 3), (81, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 3, 3, 3), (864, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3, 3), (1728, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 128, 3, 3, 3), (3456, 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((128, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (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])
    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
from torch import nn


class ConvEncoder3D(nn.Module):
    """ Simple convolutional conditioning network.

    It consists of 6 convolutional layers, each downsampling the input by a
    factor of 2, and a final fully-connected layer projecting the output to
    c_dim dimensions.
    """

    def __init__(self, c_dim=128, hidden_dim=32, **kwargs):
        """ Initialisation.

        Args:
            c_dim (int): output dimension of the latent embedding
        """
        super().__init__()
        self.conv0 = nn.Conv3d(3, hidden_dim, 3, stride=(1, 2, 2), padding=1)
        self.conv1 = nn.Conv3d(hidden_dim, hidden_dim * 2, 3, stride=(2, 2,
            2), padding=1)
        self.conv2 = nn.Conv3d(hidden_dim * 2, hidden_dim * 4, 3, stride=(1,
            2, 2), padding=1)
        self.conv3 = nn.Conv3d(hidden_dim * 4, hidden_dim * 8, 3, stride=(2,
            2, 2), padding=1)
        self.conv4 = nn.Conv3d(hidden_dim * 8, hidden_dim * 16, 3, stride=(
            2, 2, 2), padding=1)
        self.conv5 = nn.Conv3d(hidden_dim * 16, hidden_dim * 16, 3, stride=
            (2, 2, 2), padding=1)
        self.fc_out = nn.Linear(hidden_dim * 16, c_dim)
        self.actvn = nn.ReLU()

    def forward(self, x):
        x = x.transpose(1, 2)
        batch_size = x.size(0)
        net = self.conv0(x)
        net = self.conv1(self.actvn(net))
        net = self.conv2(self.actvn(net))
        net = self.conv3(self.actvn(net))
        net = self.conv4(self.actvn(net))
        net = self.conv5(self.actvn(net))
        final_dim = net.shape[1]
        net = net.view(batch_size, final_dim, -1).mean(2)
        out = self.fc_out(self.actvn(net))
        return out


def get_inputs():
    return [torch.rand([4, 3, 3, 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 import 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_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = xindex // 4096 % 3
    x2 = xindex // 12288 % 3
    x3 = xindex // 36864
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4096 * x2 + 12288 * x1 + 36864 * x3), None)
    tl.store(out_ptr0 + x4, tmp0, 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 // 3072 % 32
    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 // 512 % 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_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 // 128 % 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_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 // 16 % 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_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)
    x3 = xindex
    x1 = xindex // 4 % 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_mean_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 / tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + x2, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 3, 64, 64), (36864, 12288, 4096, 
        64, 1))
    assert_size_stride(primals_2, (32, 3, 3, 3, 3), (81, 27, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 3, 3, 3), (864, 27, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3, 3), (1728, 27, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (256, 128, 3, 3, 3), (3456, 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, (128, 512), (512, 1))
    assert_size_stride(primals_15, (128,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 3, 64, 64), (36864, 12288, 4096, 
            64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(147456)](primals_1, buf0, 
            147456, XBLOCK=1024, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 3, 32, 32), (98304, 3072, 1024, 32, 1)
            )
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(393216)](buf2, primals_3, 
            393216, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 64, 2, 16, 16), (32768, 512, 256, 16, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_2[grid(131072)](buf4, primals_5, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 2, 2),
            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, 2, 8, 8), (16384, 128, 64, 8, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_relu_3[grid(65536)](buf6, primals_7, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 1, 4, 4), (4096, 16, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_relu_4[grid(16384)](buf8, primals_9, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf9 = extern_kernels.convolution(buf8, primals_10, stride=(2, 2, 2
            ), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 1, 2, 2), (2048, 4, 4, 2, 1))
        buf10 = buf9
        del buf9
        triton_poi_fused_convolution_relu_5[grid(8192)](buf10, primals_11, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf11 = extern_kernels.convolution(buf10, primals_12, stride=(2, 2,
            2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 512, 1, 1, 1), (512, 1, 1, 1, 1))
        buf12 = reinterpret_tensor(buf11, (4, 512), (512, 1), 0)
        del buf11
        triton_poi_fused_mean_relu_6[grid(2048)](buf12, primals_13, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf13 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_15, buf12, reinterpret_tensor(
            primals_14, (512, 128), (1, 512), 0), alpha=1, beta=1, out=buf13)
        del primals_15
    return (buf13, primals_2, primals_4, primals_6, primals_8, primals_10,
        primals_12, reinterpret_tensor(primals_1, (4, 3, 3, 64, 64), (36864,
        4096, 12288, 64, 1), 0), buf2, buf4, buf6, buf8, buf10, buf12,
        primals_14)


class ConvEncoder3DNew(nn.Module):
    """ Simple convolutional conditioning network.

    It consists of 6 convolutional layers, each downsampling the input by a
    factor of 2, and a final fully-connected layer projecting the output to
    c_dim dimensions.
    """

    def __init__(self, c_dim=128, hidden_dim=32, **kwargs):
        """ Initialisation.

        Args:
            c_dim (int): output dimension of the latent embedding
        """
        super().__init__()
        self.conv0 = nn.Conv3d(3, hidden_dim, 3, stride=(1, 2, 2), padding=1)
        self.conv1 = nn.Conv3d(hidden_dim, hidden_dim * 2, 3, stride=(2, 2,
            2), padding=1)
        self.conv2 = nn.Conv3d(hidden_dim * 2, hidden_dim * 4, 3, stride=(1,
            2, 2), padding=1)
        self.conv3 = nn.Conv3d(hidden_dim * 4, hidden_dim * 8, 3, stride=(2,
            2, 2), padding=1)
        self.conv4 = nn.Conv3d(hidden_dim * 8, hidden_dim * 16, 3, stride=(
            2, 2, 2), padding=1)
        self.conv5 = nn.Conv3d(hidden_dim * 16, hidden_dim * 16, 3, stride=
            (2, 2, 2), padding=1)
        self.fc_out = nn.Linear(hidden_dim * 16, c_dim)
        self.actvn = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv0.weight
        primals_3 = self.conv0.bias
        primals_4 = self.conv1.weight
        primals_5 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.conv3.bias
        primals_10 = self.conv4.weight
        primals_11 = self.conv4.bias
        primals_12 = self.conv5.weight
        primals_13 = self.conv5.bias
        primals_14 = self.fc_out.weight
        primals_15 = self.fc_out.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]
