class_docstr: A data generator for a graph containing sequence data, created by sliding windows across the
  features of each node in a graph.
  
  .. seealso:: Model using this generator: :class:`.GCN_LSTM`.
  
  Args:
      G (StellarGraph): a graph instance where the node features are ordered sequence data
      window_size (int): the number of sequence points included in the sliding window.
      batch_size (int, optional): the number of sliding windows to include in each batch.
code: def __init__(self, G, window_size, batch_size=1):
      require_integer_in_range(window_size, "window_size", min_val=1)
      require_integer_in_range(batch_size, "batch_size", min_val=1)
  
      self.graph = G
  
      node_type = G.unique_node_type(
          "G: expected a graph with a single node type, found a graph with node types: %(found)s"
      )
      self._features = G.node_features(node_type=node_type)
      if len(self._features.shape) == 3:
          self.variates = self._features.shape[2]
      else:
          self.variates = None
  
      self.window_size = window_size
      self._batch_size = batch_size
  
file_path: stellargraph/stellargraph/mapper/sliding.py
imports: import numpy
  from .None import Generator
  from tensorflow.keras.utils import Sequence
  from ..core.validation import require_integer_in_range
label_desc: Use image node_id 1 to create a new SlidingFeaturesNodeGenerator object from inherited base classes: Generator with example: obj = SlidingFeaturesNodeGenerator(G, window_size, batch_size)