# 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/ud/cudckzwnada3nawn7jm7wuc5u4gjaoh732oq6tt7c45kaghnjl66.py
# Topologically Sorted Source Nodes: [mean, x_n, pow_1, mean_2], Original ATen: [aten.mean, aten.sub, aten.pow]
# Source node to ATen node mapping:
#   mean => mean
#   mean_2 => mean_2
#   pow_1 => pow_1
#   x_n => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
triton_per_fused_mean_pow_sub_0 = async_compile.triton('triton_per_fused_mean_pow_sub_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_pow_sub_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused_mean_pow_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = tmp0 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp3, None)
    tl.store(out_ptr1 + (tl.full([1], 0, tl.int32)), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/p6/cp6v6dvt6y5wo72c2bnmghesllthvuda6ihg2jwyyauhdplbhspa.py
# Topologically Sorted Source Nodes: [mean, x_n, mean_1, y_n, squeeze, mul_1, mean_4, neg, pow_1, mean_2, x_n_norm, pow_2, mean_3, y_n_norm, normaliser, truediv], Original ATen: [aten.mean, aten.sub, aten.squeeze, aten.mul, aten.neg, aten.pow, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   mean => mean
#   mean_1 => mean_1
#   mean_2 => mean_2
#   mean_3 => mean_3
#   mean_4 => mean_4
#   mul_1 => mul_1
#   neg => neg
#   normaliser => mul
#   pow_1 => pow_1
#   pow_2 => pow_2
#   squeeze => squeeze
#   truediv => div
#   x_n => sub
#   x_n_norm => sqrt
#   y_n => sub_1
#   y_n_norm => sqrt_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg1_1,), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %mean_1), kwargs = {})
#   %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.default](args = (%sub_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %squeeze), kwargs = {})
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_1, [0]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean_4,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%mean_2,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%mean_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %sqrt_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %mul), kwargs = {})
triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_1 = async_compile.triton('triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, '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_div_mean_mul_neg_pow_sqrt_squeeze_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + (x0), xmask)
    tmp7 = tl.load(in_ptr3 + (0))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp12 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp14 = tl.load(in_ptr2 + (64 + x0), xmask)
    tmp18 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp20 = tl.load(in_ptr2 + (128 + x0), xmask)
    tmp24 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp26 = tl.load(in_ptr2 + (192 + x0), xmask)
    tmp33 = tl.load(in_ptr4 + (0))
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp37 = tl.load(in_ptr5 + (0))
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK])
    tmp3 = 256.0
    tmp4 = tmp2 / tmp3
    tmp5 = tmp0 - tmp4
    tmp9 = tmp8 / tmp3
    tmp10 = tmp6 - tmp9
    tmp11 = tmp5 * tmp10
    tmp13 = tmp12 - tmp4
    tmp15 = tmp14 - tmp9
    tmp16 = tmp13 * tmp15
    tmp17 = tmp11 + tmp16
    tmp19 = tmp18 - tmp4
    tmp21 = tmp20 - tmp9
    tmp22 = tmp19 * tmp21
    tmp23 = tmp17 + tmp22
    tmp25 = tmp24 - tmp4
    tmp27 = tmp26 - tmp9
    tmp28 = tmp25 * tmp27
    tmp29 = tmp23 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = -tmp31
    tmp35 = tmp34 / tmp3
    tmp36 = libdevice.sqrt(tmp35)
    tmp39 = tmp38 / tmp3
    tmp40 = libdevice.sqrt(tmp39)
    tmp41 = tmp36 * tmp40
    tmp42 = tmp32 / tmp41
    tl.store(in_out_ptr0 + (x0), tmp42, 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((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [mean, x_n, pow_1, mean_2], Original ATen: [aten.mean, aten.sub, aten.pow]
        stream0 = get_raw_stream(0)
        triton_per_fused_mean_pow_sub_0.run(arg0_1, buf0, buf3, 1, 256, grid=grid(1), stream=stream0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf4 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, y_n, pow_2, mean_3], Original ATen: [aten.mean, aten.sub, aten.pow]
        triton_per_fused_mean_pow_sub_0.run(arg1_1, buf1, buf4, 1, 256, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [mean, x_n, mean_1, y_n, squeeze, mul_1, mean_4, neg, pow_1, mean_2, x_n_norm, pow_2, mean_3, y_n_norm, normaliser, truediv], Original ATen: [aten.mean, aten.sub, aten.squeeze, aten.mul, aten.neg, aten.pow, aten.sqrt, aten.div]
        triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_1.run(buf5, arg0_1, buf0, arg1_1, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        del buf3
        del buf4
    return (buf5, )


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


class PearsonCorrelation(nn.Module):
    """
    Module for measuring Pearson correlation.
    Given samples (x, y), the Pearson correlation coefficient is given by:
    .. math::
        r = rac{{}\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}
        {\\sqrt{\\sum_{i=1}^{n} (x_i - \\overline{x})^2(y_i - \\overline{y})^2}}
    """

    def __init__(self) ->None:
        super(PearsonCorrelation, self).__init__()

    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Override the forward function in nn.Modules. Performs Pearson
        Correlation calculation between ``score`` and ``mos``

        Parameters
        ----------
        x : torch.Tensor
            First input vector.
        y : torch.Tensor
            Second input vector.
        TODO: dimensions of the input data have to agree, have to be batch wise
              and check for NaN values in the result.
        """
        x_n = x - torch.mean(x)
        y_n = y - torch.mean(y)
        x_n_norm = torch.sqrt(torch.mean(x_n ** 2))
        y_n_norm = torch.sqrt(torch.mean(y_n ** 2))
        normaliser = x_n_norm * y_n_norm
        return -torch.mean(x_n * y_n.squeeze(), dim=0) / normaliser


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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_per_fused_mean_pow_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel
    ):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = tmp0 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp10, None)


@triton.jit
def triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp7 = tl.load(in_ptr3 + 0)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp12 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp14 = tl.load(in_ptr2 + (64 + x0), xmask)
    tmp18 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp20 = tl.load(in_ptr2 + (128 + x0), xmask)
    tmp24 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp26 = tl.load(in_ptr2 + (192 + x0), xmask)
    tmp33 = tl.load(in_ptr4 + 0)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp37 = tl.load(in_ptr5 + 0)
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK])
    tmp3 = 256.0
    tmp4 = tmp2 / tmp3
    tmp5 = tmp0 - tmp4
    tmp9 = tmp8 / tmp3
    tmp10 = tmp6 - tmp9
    tmp11 = tmp5 * tmp10
    tmp13 = tmp12 - tmp4
    tmp15 = tmp14 - tmp9
    tmp16 = tmp13 * tmp15
    tmp17 = tmp11 + tmp16
    tmp19 = tmp18 - tmp4
    tmp21 = tmp20 - tmp9
    tmp22 = tmp19 * tmp21
    tmp23 = tmp17 + tmp22
    tmp25 = tmp24 - tmp4
    tmp27 = tmp26 - tmp9
    tmp28 = tmp25 * tmp27
    tmp29 = tmp23 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = -tmp31
    tmp35 = tmp34 / tmp3
    tmp36 = libdevice.sqrt(tmp35)
    tmp39 = tmp38 / tmp3
    tmp40 = libdevice.sqrt(tmp39)
    tmp41 = tmp36 * tmp40
    tmp42 = tmp32 / tmp41
    tl.store(in_out_ptr0 + x0, tmp42, 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((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mean_pow_sub_0[grid(1)](arg0_1, buf0, buf3, 1, 256,
            num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf4 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_mean_pow_sub_0[grid(1)](arg1_1, buf1, buf4, 1, 256,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = buf2
        del buf2
        triton_poi_fused_div_mean_mul_neg_pow_sqrt_squeeze_sub_1[grid(64)](buf5
            , arg0_1, buf0, arg1_1, buf1, buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        del buf3
        del buf4
    return buf5,


class PearsonCorrelationNew(nn.Module):
    """
    Module for measuring Pearson correlation.
    Given samples (x, y), the Pearson correlation coefficient is given by:
    .. math::
        r = rac{{}\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}
        {\\sqrt{\\sum_{i=1}^{n} (x_i - \\overline{x})^2(y_i - \\overline{y})^2}}
    """

    def __init__(self) ->None:
        super(PearsonCorrelationNew, self).__init__()

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