# 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_2/inductor_cache/cx/ccxx7ogetsg5xueniw76nfwj5k7icpgxq2up6h7z7ykwrncdxbj5.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': '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_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 139240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3481) % 10
    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')


# kernel path: runs/run_shard_2/inductor_cache/o7/co7ls5kqvghq3jdq5zcjmuo3qappfgmqrbllrrjk6ktqptzazlav.py
# Topologically Sorted Source Nodes: [max_pool2d, out], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
#   max_pool2d => _low_memory_max_pool2d_with_offsets, getitem_1
#   out => relu
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: '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_max_pool2d_with_indices_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 33640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 29
    x1 = (xindex // 29) % 29
    x4 = (xindex // 841)
    x3 = (xindex // 8410)
    x5 = xindex % 8410
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (118*x1) + (3481*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (118*x1) + (3481*x4)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (59 + (2*x0) + (118*x1) + (3481*x4)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (60 + (2*x0) + (118*x1) + (3481*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x5 + (8448*x3)), tmp15, xmask)
    tl.store(out_ptr1 + (x6), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/5l/c5l7ltf344otnjpfyaapq4qwqey4ylylng5iqyy7djd4lehvdg53.py
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_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_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 54080
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 676) % 20
    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')


# kernel path: runs/run_shard_2/inductor_cache/hl/chltjbp3rnxoqp5o76byg2epbh4ocgaan53kmzid6lr5nk7vwfqh.py
# Topologically Sorted Source Nodes: [max_pool2d_1, out_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_with_offsets_1, getitem_3
#   out_1 => relu_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: '*i1', 4: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_relu_threshold_backward_3(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 13520
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 13
    x4 = (xindex // 13)
    x2 = (xindex // 3380)
    x3 = xindex % 3380
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (52*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (52*x4)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (26 + (2*x0) + (52*x4)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (27 + (2*x0) + (52*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x3 + (3456*x2)), tmp15, xmask)
    tl.store(out_ptr1 + (x3 + (3392*x2)), tmp18, xmask)
    tl.store(out_ptr2 + (x3 + (3456*x2)), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/xd/cxdcrcgytik6vvilb4a4snbnj2k27uyl5mifs4jfk7mmfh7rmw5k.py
# Topologically Sorted Source Nodes: [max_pool2d_1, out_1, out_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.view]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_with_offsets_1
#   out_1 => relu_1
#   out_2 => view
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_2,), kwargs = {})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu_1, [-1, 80]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_view_4 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_view_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_max_pool2d_with_indices_relu_view_4', '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_max_pool2d_with_indices_relu_view_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 13520
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((3392*(x0 // 3380)) + (x0 % 3380)), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/7h/c7h5654sbj3tz2yxhfkd7tqafczrfvsxn47vvv4yxfk7d32lve6r.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   out_3 => relu_2
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_7), kwargs = {})
#   %relu_2 : [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=[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_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_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_2/inductor_cache/r2/cr2vlnbn37rn7s42pd3fijrsi3osnwx3uoqyjfnptc63nggaq2gy.py
# Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   out_4 => relu_3
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
#   %relu_3 : [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=[4096], 
    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_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_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2704
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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')


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 = args
    args.clear()
    assert_size_stride(primals_1, (10, 1, 6, 6), (36, 36, 6, 1))
    assert_size_stride(primals_2, (10, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (20, 10, 4, 4), (160, 16, 4, 1))
    assert_size_stride(primals_5, (20, ), (1, ))
    assert_size_stride(primals_6, (32, 80), (80, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (16, 32), (32, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (1, 16), (16, 1))
    assert_size_stride(primals_11, (1, ), (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=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 59, 59), (34810, 3481, 59, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 139240, grid=grid(139240), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 29, 29), (8448, 841, 29, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 10, 29, 29), (8410, 841, 29, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d, out], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
        triton_poi_fused_max_pool2d_with_indices_relu_1.run(buf1, buf2, buf3, 33640, grid=grid(33640), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 26, 26), (13520, 676, 26, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf5, primals_5, 54080, grid=grid(54080), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 13, 13), (3456, 169, 13, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 20, 13, 13), (3392, 169, 13, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 20, 13, 13), (3456, 169, 13, 1), torch.bool)
        # Topologically Sorted Source Nodes: [max_pool2d_1, out_1], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.threshold_backward]
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3.run(buf5, buf6, buf7, buf15, 13520, grid=grid(13520), stream=stream0)
        buf8 = empty_strided_cuda((169, 80), (80, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d_1, out_1, out_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu, aten.view]
        triton_poi_fused_max_pool2d_with_indices_relu_view_4.run(buf7, buf8, 13520, grid=grid(13520), stream=stream0)
        del buf7
        buf9 = empty_strided_cuda((169, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf8, reinterpret_tensor(primals_6, (80, 32), (1, 80), 0), out=buf9)
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_5.run(buf10, primals_7, 5408, grid=grid(5408), stream=stream0)
        del primals_7
        buf11 = empty_strided_cuda((169, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf10, reinterpret_tensor(primals_8, (32, 16), (1, 32), 0), out=buf11)
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf12, primals_9, 2704, grid=grid(2704), stream=stream0)
        del primals_9
        buf14 = empty_strided_cuda((169, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf12, reinterpret_tensor(primals_10, (16, 1), (1, 16), 0), alpha=1, beta=1, out=buf14)
        del primals_11
    return (buf14, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5, buf6, buf8, buf10, buf12, primals_10, primals_8, primals_6, buf15, )


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((10, 1, 6, 6), (36, 36, 6, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((20, 10, 4, 4), (160, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 80), (80, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    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._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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 139240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3481 % 10
    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)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 33640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 29
    x1 = xindex // 29 % 29
    x4 = xindex // 841
    x3 = xindex // 8410
    x5 = xindex % 8410
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 118 * x1 + 3481 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 118 * x1 + 3481 * x4), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (59 + 2 * x0 + 118 * x1 + 3481 * x4), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (60 + 2 * x0 + 118 * x1 + 3481 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x5 + 8448 * x3), tmp15, xmask)
    tl.store(out_ptr1 + x6, tmp18, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 54080
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 676 % 20
    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)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3(in_ptr0,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 13520
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 13
    x4 = xindex // 13
    x2 = xindex // 3380
    x3 = xindex % 3380
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 52 * x4), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 52 * x4), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (26 + 2 * x0 + 52 * x4), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (27 + 2 * x0 + 52 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = 0.0
    tmp20 = tmp18 <= tmp19
    tl.store(out_ptr0 + (x3 + 3456 * x2), tmp15, xmask)
    tl.store(out_ptr1 + (x3 + 3392 * x2), tmp18, xmask)
    tl.store(out_ptr2 + (x3 + 3456 * x2), tmp20, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_view_4(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 13520
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (3392 * (x0 // 3380) + x0 % 3380), xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 5408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2704
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    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)


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) = args
    args.clear()
    assert_size_stride(primals_1, (10, 1, 6, 6), (36, 36, 6, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (20, 10, 4, 4), (160, 16, 4, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (32, 80), (80, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (16, 32), (32, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (1, 16), (16, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 59, 59), (34810, 3481, 59, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(139240)](buf1, primals_2, 
            139240, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 29, 29), (8448, 841, 29, 1),
            torch.int8)
        buf3 = empty_strided_cuda((4, 10, 29, 29), (8410, 841, 29, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(33640)](buf1,
            buf2, buf3, 33640, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 26, 26), (13520, 676, 26, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(54080)](buf5, primals_5, 54080,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 13, 13), (3456, 169, 13, 1),
            torch.int8)
        buf7 = empty_strided_cuda((4, 20, 13, 13), (3392, 169, 13, 1),
            torch.float32)
        buf15 = empty_strided_cuda((4, 20, 13, 13), (3456, 169, 13, 1),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_3[grid
            (13520)](buf5, buf6, buf7, buf15, 13520, XBLOCK=128, num_warps=
            4, num_stages=1)
        buf8 = empty_strided_cuda((169, 80), (80, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_view_4[grid(13520)](buf7,
            buf8, 13520, XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((169, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_6, (80, 32), (1,
            80), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_relu_5[grid(5408)](buf10, primals_7, 5408, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf11 = empty_strided_cuda((169, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_8, (32, 16), (1,
            32), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_6[grid(2704)](buf12, primals_9, 2704, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf14 = empty_strided_cuda((169, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf12, reinterpret_tensor(
            primals_10, (16, 1), (1, 16), 0), alpha=1, beta=1, out=buf14)
        del primals_11
    return (buf14, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
        buf6, buf8, buf10, buf12, primals_10, primals_8, primals_6, buf15)


class SlideNetNew(nn.Module):
    """
    Slided window network
    """

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=6)
        self.conv2 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size=4)
        self.fc1 = nn.Linear(in_features=80, out_features=32)
        self.fc2 = nn.Linear(in_features=32, out_features=16)
        self.fc3 = nn.Linear(in_features=16, out_features=1)

    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.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_10 = self.fc3.weight
        primals_11 = self.fc3.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])
        return output[0]
