docstr: Load this dataset into an undirected homogeneous graph, downloading it if required.
  
  The node feature vectors are included.
  
  Args:
      largest_connected_component_only (bool): if True, returns only the largest connected
          component, not the whole graph.
  
  Returns:
      A tuple where the first element is the :class:`.StellarGraph` object with the nodes, node
      feature vectors and edges, and the second element is a pandas Series of the node subject
      class labels.
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code: def load(self, largest_connected_component_only=False):
      """
      Load this dataset into an undirected homogeneous graph, downloading it if required.
  
      The node feature vectors are included.
  
      Args:
          largest_connected_component_only (bool): if True, returns only the largest connected
              component, not the whole graph.
  
      Returns:
          A tuple where the first element is the :class:`.StellarGraph` object with the nodes, node
          feature vectors and edges, and the second element is a pandas Series of the node subject
          class labels.
      """
      # some node IDs are integers like 100157 and some are strings like
      # bradshaw97introduction. Pandas can get confused, so it's best to explicitly force them all
      # to be treated as strings.
      nodes_dtype = str
  
      return _load_cora_or_citeseer(
          self,
          False,
          largest_connected_component_only,
          False,
          None,
          nodes_dtype,
      )
  
imports: from .dataset_loader import DatasetLoader
  from ..core.graph import StellarGraph, StellarDiGraph
  import itertools
  import logging
  import os
  import pandas
  import numpy
  from sklearn import preprocessing
  from ..core.experimental import experimental
label_desc: Use image node_id 1 for calling the CiteSeer obj's underlying member method code with example usage: obj.load(largest_connected_component_only) and returns: _load_cora_or_citeseer