code: def _split_tensor(t, split_indices):
      """
      Split a tensor into a list of tensors based on split_indices.
      """
      # TODO: Switch to something below
      # return [t[i:j] for i, j in zip(split_indices[:-1], split_indices[1:])]
      if split_indices.device.type != "cpu":
          split_indices = split_indices.to("cpu")
      return torch.tensor_split(t, split_indices)
  
imports: from __future__ import annotations
  from typing import List, Tuple, Dict, Optional
  from collections import defaultdict
  import cudf
  from cugraph.utilities.utils import import_optional
  from cugraph_dgl.nn import SparseGraph
label_desc: Use image node_id 2 for calling a global function with example usage: _split_tensor(t, split_indices) and returns: torch