class_docstr: Represent a single graph with additional attributes.
class_docstr_tok: ["Represent","a","single","graph","with","additional","attributes","."]
docstr: Get the node feature dictionary of the `nodes`
  
  Parameters
  ----------
  nodes: int or slice
      The nodes to be accessed
  
  Returns
  -------
  node_features: dict
      The reference dict of the actual tensor
code: def get_node_features(
      self, nodes: Union[int, slice]
  ) -> Dict[str, torch.Tensor]:
      """
      Get the node feature dictionary of the `nodes`
  
      Parameters
      ----------
      nodes: int or slice
          The nodes to be accessed
  
      Returns
      -------
      node_features: dict
          The reference dict of the actual tensor
      """
      ret = dict()
      for key in self._node_features.keys():
          if self._node_features[key] is None:
              ret[key] = None
          else:
              ret[key] = self._node_features[key][nodes]
      return ret
  
imports: import os
  import warnings
  from collections import namedtuple
  from typing import Any, Callable, Dict, List, Tuple, Union
  import dgl
  import scipy.sparse
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from .utils import EdgeNotFoundException, SizeMismatchException, check_and_expand, entail_zero_padding, int_to_list, slice_to_list
  from .views import BatchEdgeFeatView, BatchNodeFeatView, EdgeView, NodeFeatView, NodeView
label_desc: Use image node_id 7 for calling the GraphData obj's underlying member method code with example usage: obj.get_node_features(nodes) and returns: ret