code: def index_sort(
      inputs: Tensor,
      max_value: Optional[int] = None,
      stable: bool = False,
  ) -> Tuple[Tensor, Tensor]:
      r"""Sorts the elements of the :obj:`inputs` tensor in ascending order.
      It is expected that :obj:`inputs` is one-dimensional and that it only
      contains positive integer values. If :obj:`max_value` is given, it can
      be used by the underlying algorithm for better performance.
  
      Args:
          inputs (torch.Tensor): A vector with positive integer values.
          max_value (int, optional): The maximum value stored inside
              :obj:`inputs`. This value can be an estimation, but needs to be
              greater than or equal to the real maximum.
              (default: :obj:`None`)
          stable (bool, optional): Makes the sorting routine stable, which
              guarantees that the order of equivalent elements is preserved.
              (default: :obj:`False`)
      """
      if (
          stable
          or not torch_geometric.typing.WITH_INDEX_SORT
          or is_compiling()
      ):
          return inputs.sort(stable=stable)
      return pyg_lib.ops.index_sort(inputs, max_value=max_value)
  
imports: from typing import Optional, Tuple
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric import is_compiling
  from torch_geometric.typing import pyg_lib
label_desc: Use image node_id 1 for calling a global function with example usage: index_sort(inputs, max_value, stable) and returns: pyg_lib, inputs