# 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/ne/cnek2kyc2lfmkak6sjslhuyr57jc2v2swdxke5dohb5z6hxy62jr.py
# Topologically Sorted Source Nodes: [conv2d, fea], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   fea => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[131072], 
    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_leaky_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_leaky_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 81920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 5
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nl/cnl55idjy3jxmf6ziqbi4hwv66dhs7wbnteaco2wcjjslpb7sotx.py
# Topologically Sorted Source Nodes: [fea_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   fea_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tf/ctfdh7q6f3aguweuhcrsnbjpoahxxgpzsirapc6nnwpshknn6ssq.py
# Topologically Sorted Source Nodes: [conv2d_2, fea_3], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   fea_3 => gt_2, mul_2, where_2
# Graph fragment:
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused_convolution_leaky_relu_2 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[32768], 
    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_leaky_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_leaky_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 5
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ck/cckwfhtqauwarijbf4oktcmecw4wco65vxa4nvz67izghpx6mo43.py
# Topologically Sorted Source Nodes: [fea_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   fea_5 => getitem_2, getitem_3
# Graph fragment:
#   %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/av/cavvwsblj4eesengjnj5x3ztl252eca4k6bzjzafwfdlc6u6vu5o.py
# Topologically Sorted Source Nodes: [conv2d_4, fea_6], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   fea_6 => gt_4, mul_4, where_4
# Graph fragment:
#   %convolution_4 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_4, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %convolution_4, %mul_4), kwargs = {})
triton_poi_fused_convolution_leaky_relu_4 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[8192], 
    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_leaky_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_leaky_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 256) % 5
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2c/c2cjdygl6kofnyyqymxlec7j6uzfn2remgsunuqhhzwahloe5u4g.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_12, %primals_13, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_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=[4096], 
    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_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_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 256) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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 = args
    args.clear()
    assert_size_stride(primals_1, (5, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (5, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_5, (5, ), (1, ))
    assert_size_stride(primals_6, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_7, (5, ), (1, ))
    assert_size_stride(primals_8, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_9, (5, ), (1, ))
    assert_size_stride(primals_10, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_11, (5, ), (1, ))
    assert_size_stride(primals_12, (3, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_13, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 5, 64, 64), (20480, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, fea], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf1, primals_2, 81920, grid=grid(81920), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 5, 64, 64), (20480, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, fea_1], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf3, primals_5, 81920, grid=grid(81920), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 5, 32, 32), (5120, 1024, 32, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 5, 32, 32), (5120, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [fea_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 20480, grid=grid(20480), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 5, 32, 32), (5120, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, fea_3], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf7, primals_7, 20480, grid=grid(20480), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 5, 32, 32), (5120, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, fea_4], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_2.run(buf9, primals_9, 20480, grid=grid(20480), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 5, 16, 16), (1280, 256, 16, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 5, 16, 16), (1280, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [fea_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf9, buf10, buf11, 5120, grid=grid(5120), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 5, 16, 16), (1280, 256, 16, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, fea_6], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_4.run(buf13, primals_11, 5120, grid=grid(5120), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 3, 16, 16), (768, 256, 16, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf15, primals_13, 3072, grid=grid(3072), stream=stream0)
        del primals_13
    return (buf15, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, buf1, buf3, buf4, buf5, buf7, buf9, buf10, buf11, buf13, )


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((5, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((5, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((5, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((5, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((5, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((3, 5, 3, 3), (45, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
from torch import autograd as autograd
import torch.fft
from itertools import product as product


class Simple_AUG(nn.Module):

    def __init__(self, in_nc=3, out_nc=3, nf=5):
        super(Simple_AUG, self).__init__()
        self.c1 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
        self.c2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c6 = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        self.mp = nn.MaxPool2d(2)

    def forward(self, x):
        fea = self.lrelu(self.c1(x))
        fea = self.lrelu(self.c2(fea))
        fea = self.mp(fea)
        fea = self.lrelu(self.c3(fea))
        fea = self.lrelu(self.c4(fea))
        fea = self.mp(fea)
        fea = self.lrelu(self.c5(fea))
        out = self.c6(fea)
        return out


def get_inputs():
    return [torch.rand([4, 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
import torch.nn as nn
from torch import autograd as autograd
import torch.fft
from itertools import product as product
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_leaky_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 // 4096 % 5
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp16, None)


@triton.jit
def triton_poi_fused_convolution_leaky_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 // 1024 % 5
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 256 % 5
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 256 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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) = args
    args.clear()
    assert_size_stride(primals_1, (5, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (5,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_5, (5,), (1,))
    assert_size_stride(primals_6, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_7, (5,), (1,))
    assert_size_stride(primals_8, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_9, (5,), (1,))
    assert_size_stride(primals_10, (5, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_11, (5,), (1,))
    assert_size_stride(primals_12, (3, 5, 3, 3), (45, 9, 3, 1))
    assert_size_stride(primals_13, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 5, 64, 64), (20480, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(81920)](buf1,
            primals_2, 81920, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 5, 64, 64), (20480, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_leaky_relu_0[grid(81920)](buf3,
            primals_5, 81920, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 5, 32, 32), (5120, 1024, 32, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 5, 32, 32), (5120, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(20480)](buf3, buf4,
            buf5, 20480, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 5, 32, 32), (5120, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_leaky_relu_2[grid(20480)](buf7,
            primals_7, 20480, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 5, 32, 32), (5120, 1024, 32, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_leaky_relu_2[grid(20480)](buf9,
            primals_9, 20480, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 5, 16, 16), (1280, 256, 16, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 5, 16, 16), (1280, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(5120)](buf9, buf10,
            buf11, 5120, XBLOCK=256, num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 5, 16, 16), (1280, 256, 16, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_leaky_relu_4[grid(5120)](buf13,
            primals_11, 5120, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 3, 16, 16), (768, 256, 16, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_5[grid(3072)](buf15, primals_13, 3072,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
    return (buf15, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, buf1, buf3, buf4, buf5, buf7, buf9, buf10,
        buf11, buf13)


class Simple_AUGNew(nn.Module):

    def __init__(self, in_nc=3, out_nc=3, nf=5):
        super(Simple_AUGNew, self).__init__()
        self.c1 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
        self.c2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.c6 = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        self.mp = nn.MaxPool2d(2)

    def forward(self, input_0):
        primals_1 = self.c1.weight
        primals_2 = self.c1.bias
        primals_4 = self.c2.weight
        primals_5 = self.c2.bias
        primals_6 = self.c3.weight
        primals_7 = self.c3.bias
        primals_8 = self.c4.weight
        primals_9 = self.c4.bias
        primals_10 = self.c5.weight
        primals_11 = self.c5.bias
        primals_12 = self.c6.weight
        primals_13 = self.c6.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])
        return output[0]
