code: def select(
      src: Union[Tensor, List[Any], TensorFrame],
      index_or_mask: Tensor,
      dim: int,
  ) -> Union[Tensor, List[Any]]:
      r"""Selects the input tensor or input list according to a given index or
      mask vector.
  
      Args:
          src (torch.Tensor or list): The input tensor or list.
          index_or_mask (torch.Tensor): The index or mask vector.
          dim (int): The dimension along which to select.
      """
      if isinstance(src, Tensor):
          if index_or_mask.dtype == torch.bool:
              return mask_select(src, dim, index_or_mask)
          return src.index_select(dim, index_or_mask)
  
      if isinstance(src, (tuple, list)):
          if dim != 0:
              raise ValueError(
                  "Cannot select along dimension other than 0"
              )
          if index_or_mask.dtype == torch.bool:
              return [src[i] for i, m in enumerate(index_or_mask) if m]
          return [src[i] for i in index_or_mask]
  
      if isinstance(src, TensorFrame):
          assert dim == 0
          if index_or_mask.dtype == torch.bool:
              return mask_select(src, dim, index_or_mask)
          return src[index_or_mask]
  
      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 1 for calling a global function with example usage: select(src, index_or_mask, dim) and returns: src, unknown, src, mask_select, unknown, mask_select