code: def narrow(
      src: Union[Tensor, List[Any]], dim: int, start: int, length: int
  ) -> Union[Tensor, List[Any]]:
      r"""Narrows the input tensor or input list to the specified range.
  
      Args:
          src (torch.Tensor or list): The input tensor or list.
          dim (int): The dimension along which to narrow.
          start (int): The starting dimension.
          length (int): The distance to the ending dimension.
      """
      if isinstance(src, Tensor) and is_torch_sparse_tensor(src):
          # TODO Sparse tensors in `torch.sparse` do not yet support `narrow`.
          index = torch.arange(start, start + length, device=src.device)
          return src.index_select(dim, index)
  
      if isinstance(src, Tensor):
          return src.narrow(dim, start, length)
  
      if isinstance(src, list):
          if dim != 0:
              raise ValueError(
                  "Cannot narrow along dimension other than 0"
              )
          return src[start : start + length]
  
      raise ValueError(
          f"Encountered invalid input type (got '{type(src)}')"
      )
  
imports: from typing import Any, List, Union
  import torch
  from torch import Tensor
  from torch_geometric.typing import TensorFrame
  from torch_geometric.utils.mask import mask_select
  from torch_geometric.utils.sparse import is_torch_sparse_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: narrow(src, dim, start, length) and returns: src, src, src