class_docstr: A data generator for use with graph classification algorithms.
  
  The supplied graphs should be :class:`.StellarGraph` objects with node features.
  Use the :meth:`flow` method supplying the graph indexes and (optionally) targets
  to get an object that can be used as a Keras data generator.
  
  This generator supplies the features arrays and the adjacency matrices to a mini-batch Keras
  graph classification model. Differences in the number of nodes are resolved by padding each
  batch of features and adjacency matrices, and supplying a boolean mask indicating which are
  valid and which are padding.
  
  .. seealso::
  
     Models using this generator: :class:`.GCNSupervisedGraphClassification`, :class:`.DeepGraphCNN`.
  
     Examples using this generator:
  
     - `graph classification with GCN <https://stellargraph.readthedocs.io/en/stable/demos/graph-classification/gcn-supervised-graph-classification.html>`__
     - `graph classification with Deep Graph CNN <https://stellargraph.readthedocs.io/en/stable/demos/graph-classification/dgcnn-graph-classification.html>`__
     - `unsupervised graph representation learning <https://stellargraph.readthedocs.io/en/stable/demos/embeddings/gcn-unsupervised-graph-embeddings.html>`__
  
  Args:
      graphs (list): a collection of StellarGraph objects
      name (str): an optional name of the generator
class_docstr_tok: ["A","data","generator","for","use","with","graph","classification","algorithms",".","The","supplied","graphs","should","be",":","class",":","`",".StellarGraph","`","objects","with","node","features",".","Use","the",":","meth",":","`","flow","`","method","supplying","the","graph","indexes","and","(","optionally",")","targets","to","get","an","object","that","can","be","used","as","a","Keras","data","generator",".","This","generator","supplies","the","features","arrays","and","the","adjacency","matrices","to","a","mini-batch","Keras","graph","classification","model",".","Differences","in","the","number","of","nodes","are","resolved","by","padding","each","batch","of","features","and","adjacency","matrices",",","and","supplying","a","boolean","mask","indicating","which","are","valid","and","which","are","padding",".","..","seealso",":",":","Models","using","this","generator",":",":","class",":","`",".GCNSupervisedGraphClassification","`",",",":","class",":","`",".DeepGraphCNN","`",".","Examples","using","this","generator",":","-","`","graph","classification","with","GCN","<","https",":","\/\/stellargraph.readthedocs.io\/en\/stable\/demos\/graph-classification\/gcn-supervised-graph-classification.html",">","`","__","-","`","graph","classification","with","Deep","Graph","CNN","<","https",":","\/\/stellargraph.readthedocs.io\/en\/stable\/demos\/graph-classification\/dgcnn-graph-classification.html",">","`","__","-","`","unsupervised","graph","representation","learning","<","https",":","\/\/stellargraph.readthedocs.io\/en\/stable\/demos\/embeddings\/gcn-unsupervised-graph-embeddings.html",">","`","__","Args",":","graphs","(","list",")",":","a","collection","of","StellarGraph","objects","name","(","str",")",":","an","optional","name","of","the","generator"]
code: def __init__(self, graphs, name=None):
      self.node_features_size = None
      self._check_graphs(graphs)
  
      self.graphs = graphs
      self.name = name
  
imports: from ..core.graph import StellarGraph
  from ..core.utils import is_real_iterable, normalize_adj
  from ..random import random_state
  import numpy
  from tensorflow.keras.utils import Sequence
  from .base import Generator
label_desc: Use image node_id 1 to create a new PaddedGraphGenerator object from inherited base classes: Generator with example: obj = PaddedGraphGenerator(graphs, name)