class_docstr: Abstract base class for graph data generators from Neo4j.
  
  The supplied graph should be a StellarGraph object with node features.
  
  Do not use this base class: use a subclass specific to the method.
  
  Args:
      graph (Neo4jStellarGraph): The machine-learning ready graph.
      batch_size (int): Size of batch to return.
      schema (GraphSchema): [Optional] Schema for the graph, for heterogeneous graphs.
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code: def flow(self, node_ids, targets=None, shuffle=False, seed=None):
      return NodeSequence(
          self.sample_features,
          self.batch_size,
          node_ids,
          targets,
          shuffle=shuffle,
          seed=seed,
      )
  
file_path: stellargraph/stellargraph/connector/neo4j/mapper.py
imports: import numpy
  import random
  from .sampler import Neo4jDirectedBreadthFirstNeighbors, Neo4jSampledBreadthFirstWalk
  from ...core.graph import GraphSchema
  from ...mapper import NodeSequence
  from ...mapper.sampled_node_generators import BatchedNodeGenerator
  from ...core.experimental import experimental
  from .graph import Neo4jStellarGraph
label_desc: Use image node_id 2 for calling the Neo4jBatchedNodeGenerator obj's underlying member method code with example usage: obj.flow(node_ids, targets, shuffle, seed) and returns: NodeSequence