class_docstr: Graph Convolution (GCN) Keras layer.
  The implementation is based on https://github.com/tkipf/keras-gcn.
  
  Original paper: Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling,
  International Conference on Learning Representations (ICLR), 2017 https://github.com/tkipf/gcn
  
  Notes:
    - The inputs are 3 dimensional tensors: batch size, sequence length, and number of nodes.
    - This class assumes that a simple unweighted or weighted adjacency matrix is passed to it,
      the normalized Laplacian matrix is calculated within the class.
  
  Args:
      units (int): dimensionality of output feature vectors
      A (N x N): weighted/unweighted adjacency matrix
      activation (str or func): nonlinear activation applied to layer's output to obtain output features
      use_bias (bool): toggles an optional bias
      kernel_initializer (str or func, optional): The initialiser to use for the weights.
      kernel_regularizer (str or func, optional): The regulariser to use for the weights.
      kernel_constraint (str or func, optional): The constraint to use for the weights.
      bias_initializer (str or func, optional): The initialiser to use for the bias.
      bias_regularizer (str or func, optional): The regulariser to use for the bias.
      bias_constraint (str or func, optional): The constraint to use for the bias.
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docstr: Computes the output shape of the layer.
  Assumes the following inputs:
  
  Args:
      input_shapes (tuple of int)
          Shape tuples can include None for free dimensions, instead of an integer.
  
  Returns:
      An input shape tuple.
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code: def compute_output_shape(self, input_shapes):
      """
      Computes the output shape of the layer.
      Assumes the following inputs:
  
      Args:
          input_shapes (tuple of int)
              Shape tuples can include None for free dimensions, instead of an integer.
  
      Returns:
          An input shape tuple.
      """
      feature_shape = input_shapes
  
      return feature_shape[0], feature_shape[1], self.units
  
file_path: stellargraph/stellargraph/layer/gcn_lstm.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras import activations, initializers, constraints, regularizers
  from tensorflow.keras.layers import Input, Layer, Dropout, LSTM, Dense, Permute, Reshape
  from ..mapper import SlidingFeaturesNodeGenerator
  from ..core.experimental import experimental
  from ..core.utils import calculate_laplacian
label_desc: Use image node_id 3 for calling the FixedAdjacencyGraphConvolution obj's underlying member method code with example usage: obj.compute_output_shape(input_shapes) and returns: feature_shape,  feature_shape,  self