# 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_1/inductor_cache/rr/crrkfiuzinrqlkjsh6ixacvyrm76qb7yzuyt5euwmtbejf7u6uax.py
# Topologically Sorted Source Nodes: [lsm], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   lsm => amax, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/eh/cehokht3m5wzbe6lmjz3m6mp7d4d2eqqxpa73h2ap4rrs5cbtgo6.py
# Topologically Sorted Source Nodes: [lsm, mul, sum_1, loss, loss_1], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.mean]
# Source node to ATen node mapping:
#   loss => neg
#   loss_1 => mean
#   lsm => exp, log, sub_1, sum_1
#   mul => mul
#   sum_1 => sum_2
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sub_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_2,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%neg,), kwargs = {})
triton_per_fused__log_softmax_mean_mul_neg_sum_1 = async_compile.triton('triton_per_fused__log_softmax_mean_mul_neg_sum_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.persistent_reduction(
    size_hints=[1, 64],
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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__log_softmax_mean_mul_neg_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tmp0 * tmp13
    tmp16 = tmp3 - tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp20 = tmp6 - tmp12
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp24 = tmp9 - tmp12
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = -tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 64.0
    tmp32 = tmp30 / tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp32, None)
''', 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, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [lsm], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [lsm, mul, sum_1, loss, loss_1], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.mean]
        triton_per_fused__log_softmax_mean_mul_neg_sum_1.run(buf2, arg1_1, buf0, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
    return (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
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F


def _is_long(x):
    if hasattr(x, 'data'):
        x = x.data
    return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)


def cross_entropy(inputs, target, weight=None, ignore_index=-100, reduction
    ='mean', smooth_eps=None, smooth_dist=None, from_logits=True):
    """cross entropy loss, with support for target distributions and label smoothing https://arxiv.org/abs/1512.00567"""
    smooth_eps = smooth_eps or 0
    if _is_long(target) and smooth_eps == 0:
        if from_logits:
            return F.cross_entropy(inputs, target, weight, ignore_index=
                ignore_index, reduction=reduction)
        else:
            return F.nll_loss(inputs, target, weight, ignore_index=
                ignore_index, reduction=reduction)
    if from_logits:
        lsm = F.log_softmax(inputs, dim=-1)
    else:
        lsm = inputs
    masked_indices = None
    num_classes = inputs.size(-1)
    if _is_long(target) and ignore_index >= 0:
        masked_indices = target.eq(ignore_index)
    if smooth_eps > 0 and smooth_dist is not None:
        if _is_long(target):
            target = onehot(target, num_classes).type_as(inputs)
        if smooth_dist.dim() < target.dim():
            smooth_dist = smooth_dist.unsqueeze(0)
        target.lerp_(smooth_dist, smooth_eps)
    if weight is not None:
        lsm = lsm * weight.unsqueeze(0)
    if _is_long(target):
        eps_sum = smooth_eps / num_classes
        eps_nll = 1.0 - eps_sum - smooth_eps
        likelihood = lsm.gather(dim=-1, index=target.unsqueeze(-1)).squeeze(-1)
        loss = -(eps_nll * likelihood + eps_sum * lsm.sum(-1))
    else:
        loss = -(target * lsm).sum(-1)
    if masked_indices is not None:
        loss.masked_fill_(masked_indices, 0)
    if reduction == 'sum':
        loss = loss.sum()
    elif reduction == 'mean':
        if masked_indices is None:
            loss = loss.mean()
        else:
            loss = loss.sum() / float(loss.size(0) - masked_indices.sum())
    return loss


class CrossEntropyLoss(nn.CrossEntropyLoss):
    """CrossEntropyLoss - with ability to recieve distrbution as targets, and optional label smoothing"""

    def __init__(self, weight=None, ignore_index=-100, reduction='mean',
        smooth_eps=None, smooth_dist=None, from_logits=True):
        super(CrossEntropyLoss, self).__init__(weight=weight, ignore_index=
            ignore_index, reduction=reduction)
        self.smooth_eps = smooth_eps
        self.smooth_dist = smooth_dist
        self.from_logits = from_logits

    def forward(self, input, target, smooth_dist=None):
        if smooth_dist is None:
            smooth_dist = self.smooth_dist
        return cross_entropy(input, target, weight=self.weight,
            ignore_index=self.ignore_index, reduction=self.reduction,
            smooth_eps=self.smooth_eps, smooth_dist=smooth_dist,
            from_logits=self.from_logits)


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 math as tl_math
from torch import nn
import torch.nn.functional as F
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__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tmp0 * tmp13
    tmp16 = tmp3 - tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp20 = tmp6 - tmp12
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp24 = tmp9 - tmp12
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = -tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 64.0
    tmp32 = tmp30 / tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, None)


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, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf2,
            arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def _is_long(x):
    if hasattr(x, 'data'):
        x = x.data
    return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)


def cross_entropy(inputs, target, weight=None, ignore_index=-100, reduction
    ='mean', smooth_eps=None, smooth_dist=None, from_logits=True):
    """cross entropy loss, with support for target distributions and label smoothing https://arxiv.org/abs/1512.00567"""
    smooth_eps = smooth_eps or 0
    if _is_long(target) and smooth_eps == 0:
        if from_logits:
            return F.cross_entropy(inputs, target, weight, ignore_index=
                ignore_index, reduction=reduction)
        else:
            return F.nll_loss(inputs, target, weight, ignore_index=
                ignore_index, reduction=reduction)
    if from_logits:
        lsm = F.log_softmax(inputs, dim=-1)
    else:
        lsm = inputs
    masked_indices = None
    num_classes = inputs.size(-1)
    if _is_long(target) and ignore_index >= 0:
        masked_indices = target.eq(ignore_index)
    if smooth_eps > 0 and smooth_dist is not None:
        if _is_long(target):
            target = onehot(target, num_classes).type_as(inputs)
        if smooth_dist.dim() < target.dim():
            smooth_dist = smooth_dist.unsqueeze(0)
        target.lerp_(smooth_dist, smooth_eps)
    if weight is not None:
        lsm = lsm * weight.unsqueeze(0)
    if _is_long(target):
        eps_sum = smooth_eps / num_classes
        eps_nll = 1.0 - eps_sum - smooth_eps
        likelihood = lsm.gather(dim=-1, index=target.unsqueeze(-1)).squeeze(-1)
        loss = -(eps_nll * likelihood + eps_sum * lsm.sum(-1))
    else:
        loss = -(target * lsm).sum(-1)
    if masked_indices is not None:
        loss.masked_fill_(masked_indices, 0)
    if reduction == 'sum':
        loss = loss.sum()
    elif reduction == 'mean':
        if masked_indices is None:
            loss = loss.mean()
        else:
            loss = loss.sum() / float(loss.size(0) - masked_indices.sum())
    return loss


class CrossEntropyLossNew(nn.CrossEntropyLoss):
    """CrossEntropyLoss - with ability to recieve distrbution as targets, and optional label smoothing"""

    def __init__(self, weight=None, ignore_index=-100, reduction='mean',
        smooth_eps=None, smooth_dist=None, from_logits=True):
        super(CrossEntropyLossNew, self).__init__(weight=weight,
            ignore_index=ignore_index, reduction=reduction)
        self.smooth_eps = smooth_eps
        self.smooth_dist = smooth_dist
        self.from_logits = from_logits

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