docstr: Load this dataset into a homogeneous graph that is directed or undirected, downloading it if
  required.
  
  The node feature vectors are included, and the edges are treated as directed or undirected
  depending on the ``directed`` parameter.
  
  Args:
      directed (bool): if True, return a directed graph, otherwise return an undirected one.
      largest_connected_component_only (bool): if True, returns only the largest connected
          component, not the whole graph.
      edge_weights (callable, optional): a function that accepts three parameters: an
          unweighted StellarGraph containing node features, a Pandas Series of the node
          labels, a Pandas DataFrame of the edges (with `source` and `target` columns). It
          should return a sequence of numbers (e.g. a 1D NumPy array) of edge weights for each
          edge in the DataFrame.
      str_node_ids (bool): if True, load the node IDs as strings, rather than integers.
      subject_as_feature (bool): if True, the subject for each paper (node) is included in the
          node features, one-hot encoded (the subjects are still also returned as a Series).
  
  Returns:
      A tuple where the first element is the :class:`.StellarGraph` object (or
      :class:`.StellarDiGraph`, if ``directed == True``) 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,
      directed=False,
      largest_connected_component_only=False,
      subject_as_feature=False,
      edge_weights=None,
      str_node_ids=False,
  ):
      """
      Load this dataset into a homogeneous graph that is directed or undirected, downloading it if
      required.
  
      The node feature vectors are included, and the edges are treated as directed or undirected
      depending on the ``directed`` parameter.
  
      Args:
          directed (bool): if True, return a directed graph, otherwise return an undirected one.
          largest_connected_component_only (bool): if True, returns only the largest connected
              component, not the whole graph.
          edge_weights (callable, optional): a function that accepts three parameters: an
              unweighted StellarGraph containing node features, a Pandas Series of the node
              labels, a Pandas DataFrame of the edges (with `source` and `target` columns). It
              should return a sequence of numbers (e.g. a 1D NumPy array) of edge weights for each
              edge in the DataFrame.
          str_node_ids (bool): if True, load the node IDs as strings, rather than integers.
          subject_as_feature (bool): if True, the subject for each paper (node) is included in the
              node features, one-hot encoded (the subjects are still also returned as a Series).
  
      Returns:
          A tuple where the first element is the :class:`.StellarGraph` object (or
          :class:`.StellarDiGraph`, if ``directed == True``) with the nodes, node feature vectors
          and edges, and the second element is a pandas Series of the node subject class labels.
      """
      nodes_dtype = str if str_node_ids else int
  
      return _load_cora_or_citeseer(
          self,
          directed,
          largest_connected_component_only,
          subject_as_feature,
          edge_weights,
          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 Cora obj's underlying member method code with example usage: obj.load(directed, largest_connected_component_only, subject_as_feature, edge_weights, str_node_ids) and returns: _load_cora_or_citeseer