# AOT ID: ['0_inference']
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_4/inductor_cache/d7/cd7gnc5naykwrsak74dr3vwjj57pr5unvd4b3n6p42t7adgqlns4.py
# Topologically Sorted Source Nodes: [outputs_mean], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   outputs_mean => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%arg0_1, [2, 2], [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_0 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[256], 
    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_avg_pool2d_0', '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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 3) % 3
    x0 = xindex % 3
    x2 = (xindex // 9)
    x4 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + (2*x0) + (8*x1) + (16*x2)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + (2*x0) + (8*x1) + (16*x2)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (8*x1) + (16*x2)), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (8*x1) + (16*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = 1 + ((-2)*x0) + ((-2)*x1) + (((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5)))*((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))) + ((-2)*x0*((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))) + ((-2)*x1*((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5)))) + (4*x0*x1) + ((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5))) + ((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))
    tmp30 = tmp28 / tmp29
    tl.store(out_ptr0 + (x4), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/q4/cq4cxxnd3uk4nokpwrrgnmn4dxunq3brjfsuakmfs6ygokbfba4d.py
# Topologically Sorted Source Nodes: [outputs_variance, outputs_variance_1, truediv_1], Original ATen: [aten.avg_pool2d, aten.div]
# Source node to ATen node mapping:
#   outputs_variance => avg_pool2d_1
#   outputs_variance_1 => div
#   truediv_1 => div_1
# Graph fragment:
#   %avg_pool2d_1 : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%arg1_1, [2, 2], [2, 2], [1, 1]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%avg_pool2d_1, 16), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, 16), kwargs = {})
triton_poi_fused_avg_pool2d_div_1 = async_compile.triton('triton_poi_fused_avg_pool2d_div_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=[256], 
    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_avg_pool2d_div_1', 'mutated_arg_names': ['in_out_ptr0'], '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_avg_pool2d_div_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 3) % 3
    x0 = xindex % 3
    x2 = (xindex // 9)
    x3 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + (2*x0) + (8*x1) + (16*x2)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + (2*x0) + (8*x1) + (16*x2)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (8*x1) + (16*x2)), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (8*x1) + (16*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = 1 + ((-2)*x0) + ((-2)*x1) + (((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5)))*((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))) + ((-2)*x0*((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))) + ((-2)*x1*((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5)))) + (4*x0*x1) + ((5) * ((5) <= (1 + (2*x0))) + (1 + (2*x0)) * ((1 + (2*x0)) < (5))) + ((5) * ((5) <= (1 + (2*x1))) + (1 + (2*x1)) * ((1 + (2*x1)) < (5)))
    tmp30 = tmp28 / tmp29
    tmp31 = 0.0625
    tmp32 = tmp30 * tmp31
    tmp33 = tmp32 * tmp31
    tl.store(in_out_ptr0 + (x3), tmp33, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [outputs_mean], Original ATen: [aten.avg_pool2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0.run(arg0_1, buf0, 144, grid=grid(144), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [outputs_variance, outputs_variance_1, truediv_1], Original ATen: [aten.avg_pool2d, aten.div]
        triton_poi_fused_avg_pool2d_div_1.run(buf2, arg1_1, 144, grid=grid(144), stream=stream0)
        del arg1_1
    return (buf0, buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


def keep_variance_fn(x):
    return x + 0.001


class AvgPool2d(nn.Module):

    def __init__(self, keep_variance_fn=None, kernel_size=2):
        super(AvgPool2d, self).__init__()
        self._keep_variance_fn = keep_variance_fn
        self.kernel_size = kernel_size

    def forward(self, inputs_mean, inputs_variance):
        outputs_mean = F.avg_pool2d(inputs_mean, self.kernel_size, stride=2,
            padding=1)
        outputs_variance = F.avg_pool2d(inputs_variance, self.kernel_size,
            stride=2, padding=1)
        outputs_variance = outputs_variance / (inputs_mean.size(2) *
            inputs_mean.size(3))
        if self._keep_variance_fn is not None:
            outputs_variance = self._keep_variance_fn(outputs_variance)
        return outputs_mean, outputs_variance / (inputs_mean.shape[2] *
            inputs_mean.shape[3])


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


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

import torch
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
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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 3 % 3
    x0 = xindex % 3
    x2 = xindex // 9
    x4 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x1 + 16 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x1 + 16 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x1 + 16 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = 1 + -2 * x0 + -2 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) *
        (1 + 2 * x0 < 5)) * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
        x1 < 5)) + -2 * x0 * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
        x1 < 5)) + -2 * x1 * (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 + 2 *
        x0 < 5)) + 4 * x0 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 +
        2 * x0 < 5)) + (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 * x1 < 5)
        )
    tmp30 = tmp28 / tmp29
    tl.store(out_ptr0 + x4, tmp30, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_div_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 3 % 3
    x0 = xindex % 3
    x2 = xindex // 9
    x3 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x1 + 16 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x1 + 16 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x1 + 16 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tmp27 + tmp25
    tmp29 = 1 + -2 * x0 + -2 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) *
        (1 + 2 * x0 < 5)) * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
        x1 < 5)) + -2 * x0 * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
        x1 < 5)) + -2 * x1 * (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 + 2 *
        x0 < 5)) + 4 * x0 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 +
        2 * x0 < 5)) + (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 * x1 < 5)
        )
    tmp30 = tmp28 / tmp29
    tmp31 = 0.0625
    tmp32 = tmp30 * tmp31
    tmp33 = tmp32 * tmp31
    tl.store(in_out_ptr0 + x3, tmp33, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0[grid(144)](arg0_1, buf0, 144, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        buf2 = buf1
        del buf1
        triton_poi_fused_avg_pool2d_div_1[grid(144)](buf2, arg1_1, 144,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
    return buf0, buf2


def keep_variance_fn(x):
    return x + 0.001


class AvgPool2dNew(nn.Module):

    def __init__(self, keep_variance_fn=None, kernel_size=2):
        super(AvgPool2dNew, self).__init__()
        self._keep_variance_fn = keep_variance_fn
        self.kernel_size = kernel_size

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
