# 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_8/inductor_cache/ej/cejfrwnzxinkchwn6symdb72fdtj7gix5hy2vuswodhbeh45mrae.py
# Topologically Sorted Source Nodes: [conv2d, h11], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   h11 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 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_8/inductor_cache/7z/c7zuih2ysjtir5rh5seep5ijnhokjlgkyjw2edhf257ahvz4iipr.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => 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=[262144], 
    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 = 262144
    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_8/inductor_cache/xq/cxqz2dr7nh2qabrtemj52pazmhrknj5ltcy32ka252ia6a3jgpqi.py
# Topologically Sorted Source Nodes: [conv2d_2, h21], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   h21 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = 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 = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 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_8/inductor_cache/pr/cpri5daxkfbmt5ostbhb5o2avircr64a2rmdkxfackaxyjfc7owe.py
# Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_1 => 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=[131072], 
    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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*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_8/inductor_cache/of/cof37d5wbqzvtkioj7k4me7wqpvfv55rs62ytonj7gij2o3abnod.py
# Topologically Sorted Source Nodes: [conv2d_4, h31], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   h31 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = 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 = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 256) % 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_8/inductor_cache/f6/cf6kfci6x62feotaybtxzahvqyif3sv76jew65nldxbpd6dbtzcq.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_3, %convolution_6], 1), kwargs = {})
triton_poi_fused_cat_5 = async_compile.triton('triton_poi_fused_cat_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1048576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 1024) % 256
    x0 = xindex % 1024
    x2 = (xindex // 262144)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (1024*x1) + (131072*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (1024*((-128) + x1)) + (131072*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-128) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/gl/cglq7ed2f77kyy7vjdsks4lx3mo37wlnrpctglawt5mkmxf4vkfu.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_1, %convolution_9], 1), kwargs = {})
triton_poi_fused_cat_6 = async_compile.triton('triton_poi_fused_cat_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2097152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 128
    x0 = xindex % 4096
    x2 = (xindex // 524288)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (262144*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4096*((-64) + x1)) + (262144*x2)), tmp6, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-64) + x1), tmp6, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ff/cff2jqbegfi7dejnhmjgondvbnkshr3wq7t2rbzobq2kjnoum742.py
# Topologically Sorted Source Nodes: [conv2d_10, sigmoid], Original ATen: [aten.convolution, aten.sigmoid]
# Source node to ATen node mapping:
#   conv2d_10 => convolution_12
#   sigmoid => sigmoid
# Graph fragment:
#   %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_9, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_12,), kwargs = {})
triton_poi_fused_convolution_sigmoid_7 = async_compile.triton('triton_poi_fused_convolution_sigmoid_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_sigmoid_7', '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_sigmoid_7(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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + (x0), tmp4, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256, ), (1, ))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256, ), (1, ))
    assert_size_stride(primals_14, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    assert_size_stride(primals_16, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_21, (64, ), (1, ))
    assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (64, ), (1, ))
    assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_25, (64, ), (1, ))
    assert_size_stride(primals_26, (1, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_27, (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=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, h11], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 1048576, grid=grid(1048576), 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, h12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 1048576, grid=grid(1048576), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 262144, grid=grid(262144), 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, h21], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf7, primals_7, 524288, grid=grid(524288), 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3, h22], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf9, primals_9, 524288, grid=grid(524288), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf9, buf10, buf11, 131072, grid=grid(131072), 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, 256, 16, 16), (65536, 256, 16, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, h31], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf13, primals_11, 262144, grid=grid(262144), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [conv2d_5], 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, 256, 16, 16), (65536, 256, 16, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [conv2d_5, h32], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_4.run(buf15, primals_13, 262144, grid=grid(262144), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf17 = empty_strided_cuda((4, 256, 32, 32), (262144, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_5.run(buf9, buf16, primals_15, buf17, 1048576, grid=grid(1048576), stream=stream0)
        del buf16
        del primals_15
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [conv2d_6, h41], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf19, primals_17, 524288, grid=grid(524288), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution]
        buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [conv2d_7, h42], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf21, primals_19, 524288, grid=grid(524288), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [conv_transpose2d_1], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_20, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf23 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_6.run(buf3, buf22, primals_21, buf23, 2097152, grid=grid(2097152), stream=stream0)
        del buf22
        del primals_21
        # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [conv2d_8, h51], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf25, primals_23, 1048576, grid=grid(1048576), stream=stream0)
        del primals_23
        # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [conv2d_9, h52], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf27, primals_25, 1048576, grid=grid(1048576), stream=stream0)
        del primals_25
        # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf29 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [conv2d_10, sigmoid], Original ATen: [aten.convolution, aten.sigmoid]
        triton_poi_fused_convolution_sigmoid_7.run(buf29, primals_27, 16384, grid=grid(16384), stream=stream0)
        del primals_27
    return (buf29, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf1, buf3, buf4, buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf29, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((64, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 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), (576, 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((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((256, 128, 2, 2), (512, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 64, 2, 2), (256, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((1, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = 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, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn


class Unet_2levels(nn.Module):

    def __init__(self):
        super().__init__()
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.l11 = nn.Conv2d(1, 64, 3, padding=1)
        self.l12 = nn.Conv2d(64, 64, 3, padding=1)
        self.l21 = nn.Conv2d(64, 128, 3, padding=1)
        self.l22 = nn.Conv2d(128, 128, 3, padding=1)
        self.l31 = nn.Conv2d(128, 256, 3, padding=1)
        self.l32 = nn.Conv2d(256, 256, 3, padding=1)
        self.l41 = nn.Conv2d(256, 128, 3, padding=1)
        self.l42 = nn.Conv2d(128, 128, 3, padding=1)
        self.l51 = nn.Conv2d(128, 64, 3, padding=1)
        self.l52 = nn.Conv2d(64, 64, 3, padding=1)
        self.l53 = nn.Conv2d(64, 1, 1, padding=0)
        self.up1 = nn.ConvTranspose2d(256, 128, 2, 2, padding=0,
            output_padding=0)
        self.up2 = nn.ConvTranspose2d(128, 64, 2, 2, padding=0,
            output_padding=0)

    def forward(self, x):
        h11 = self.relu(self.l11(x))
        h12 = self.relu(self.l12(h11))
        h21 = self.relu(self.l21(self.maxpool(h12)))
        h22 = self.relu(self.l22(h21))
        h31 = self.relu(self.l31(self.maxpool(h22)))
        h32 = self.relu(self.l32(h31))
        h41 = self.relu(self.l41(torch.cat([h22, self.up1(h32)], dim=1)))
        h42 = self.relu(self.l42(h41))
        h51 = self.relu(self.l51(torch.cat([h12, self.up2(h42)], dim=1)))
        h52 = self.relu(self.l52(h51))
        return self.sigmoid(self.l53(h52))


def get_inputs():
    return [torch.rand([4, 1, 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
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_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 % 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_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_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 % 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_max_pool2d_with_indices_3(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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * 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_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 // 256 % 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_cat_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 1024 % 256
    x0 = xindex % 1024
    x2 = xindex // 262144
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 131072 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 1024 * (-128 + x1) + 131072 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-128 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_cat_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 128
    x0 = xindex % 4096
    x2 = xindex // 524288
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 262144 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4096 * (-64 + x1) + 262144 * x2), tmp6,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-64 + x1), tmp6, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, None)


@triton.jit
def triton_poi_fused_convolution_sigmoid_7(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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27) = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (256, 128, 2, 2), (512, 4, 2, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 64, 2, 2), (256, 4, 2, 1))
    assert_size_stride(primals_21, (64,), (1,))
    assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (64,), (1,))
    assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_25, (64,), (1,))
    assert_size_stride(primals_26, (1, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_27, (1,), (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, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf1, primals_2,
            1048576, 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, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf3, primals_5,
            1048576, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(262144)](buf3, buf4,
            buf5, 262144, XBLOCK=512, num_warps=8, 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_2[grid(524288)](buf7, primals_7, 
            524288, XBLOCK=1024, 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, 128, 32, 32), (131072, 1024, 32, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_2[grid(524288)](buf9, primals_9, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(131072)](buf9,
            buf10, buf11, 131072, XBLOCK=512, num_warps=8, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_relu_4[grid(262144)](buf13, primals_11,
            262144, XBLOCK=512, num_warps=8, 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, 256, 16, 16), (65536, 256, 16, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_4[grid(262144)](buf15, primals_13,
            262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_13
        buf16 = extern_kernels.convolution(buf15, primals_14, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf17 = empty_strided_cuda((4, 256, 32, 32), (262144, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_cat_5[grid(1048576)](buf9, buf16, primals_15,
            buf17, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf16
        del primals_15
        buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_2[grid(524288)](buf19, primals_17,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_17
        buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_relu_2[grid(524288)](buf21, primals_19,
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_19
        buf22 = extern_kernels.convolution(buf21, primals_20, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf23 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_6[grid(2097152)](buf3, buf22, primals_21,
            buf23, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf22
        del primals_21
        buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf25,
            primals_23, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_23
        buf26 = extern_kernels.convolution(buf25, primals_24, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_relu_0[grid(1048576)](buf27,
            primals_25, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_25
        buf28 = extern_kernels.convolution(buf27, primals_26, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf28, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf29 = buf28
        del buf28
        triton_poi_fused_convolution_sigmoid_7[grid(16384)](buf29,
            primals_27, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_27
    return (buf29, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_16, primals_18,
        primals_20, primals_22, primals_24, primals_26, buf1, buf3, buf4,
        buf5, buf7, buf9, buf10, buf11, buf13, buf15, buf17, buf19, buf21,
        buf23, buf25, buf27, buf29)


class Unet_2levelsNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
            align_corners=True)
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.l11 = nn.Conv2d(1, 64, 3, padding=1)
        self.l12 = nn.Conv2d(64, 64, 3, padding=1)
        self.l21 = nn.Conv2d(64, 128, 3, padding=1)
        self.l22 = nn.Conv2d(128, 128, 3, padding=1)
        self.l31 = nn.Conv2d(128, 256, 3, padding=1)
        self.l32 = nn.Conv2d(256, 256, 3, padding=1)
        self.l41 = nn.Conv2d(256, 128, 3, padding=1)
        self.l42 = nn.Conv2d(128, 128, 3, padding=1)
        self.l51 = nn.Conv2d(128, 64, 3, padding=1)
        self.l52 = nn.Conv2d(64, 64, 3, padding=1)
        self.l53 = nn.Conv2d(64, 1, 1, padding=0)
        self.up1 = nn.ConvTranspose2d(256, 128, 2, 2, padding=0,
            output_padding=0)
        self.up2 = nn.ConvTranspose2d(128, 64, 2, 2, padding=0,
            output_padding=0)

    def forward(self, input_0):
        primals_1 = self.l11.weight
        primals_2 = self.l11.bias
        primals_4 = self.l12.weight
        primals_5 = self.l12.bias
        primals_6 = self.l21.weight
        primals_7 = self.l21.bias
        primals_8 = self.l22.weight
        primals_9 = self.l22.bias
        primals_10 = self.l31.weight
        primals_11 = self.l31.bias
        primals_12 = self.l32.weight
        primals_13 = self.l32.bias
        primals_16 = self.l41.weight
        primals_15 = self.l41.bias
        primals_18 = self.l42.weight
        primals_17 = self.l42.bias
        primals_22 = self.l51.weight
        primals_21 = self.l51.bias
        primals_24 = self.l52.weight
        primals_23 = self.l52.bias
        primals_26 = self.l53.weight
        primals_27 = self.l53.bias
        primals_14 = self.up1.weight
        primals_19 = self.up1.bias
        primals_20 = self.up2.weight
        primals_25 = self.up2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27])
        return output[0]
