class_docstr: Relational Graph Convolution (RGCN) Keras layer.
  
  Original paper: Modeling Relational Data with Graph Convolutional Networks.
  Thomas N. Kipf, Michael Schlichtkrull (2017). https://arxiv.org/pdf/1703.06103.pdf
  
  Notes:
    - The inputs are tensors with a batch dimension of 1:
      Keras requires this batch dimension, and for full-batch methods
      we only have a single "batch".
  
    - There are 1 + R inputs required (where R is the number of relationships): the node features,
      and a normalized adjacency matrix for each relationship
  
  .. seealso:: :class:`.RGCN` combines several of these layers.
  
  Args:
      units (int): dimensionality of output feature vectors
      num_relationships (int): the number of relationships in the graph
      num_bases (int): the number of basis matrices to use for parameterizing the weight matrices as described in
          the paper; defaults to 0. ``num_bases < 0`` triggers the default behaviour of ``num_bases = 0``
      activation (str or func): nonlinear activation applied to layer's output to obtain output features
      use_bias (bool): toggles an optional bias
      final_layer (bool): Deprecated, use ``tf.gather`` or :class:`.GatherIndices`
      kernel_initializer (str or func): The initialiser to use for the self kernel and also relational kernels if ``num_bases=0``.
      kernel_regularizer (str or func): The regulariser to use for the self kernel and also relational kernels if ``num_bases=0``.
      kernel_constraint (str or func): The constraint to use for the self kernel and also relational kernels if ``num_bases=0``.
      basis_initializer (str or func): The initialiser to use for the basis matrices.
      basis_regularizer (str or func): The regulariser to use for the basis matrices.
      basis_constraint (str or func): The constraint to use for the basis matrices.
      coefficient_initializer (str or func): The initialiser to use for the coefficients.
      coefficient_regularizer (str or func): The regulariser to use for the coefficients.
      coefficient_constraint (str or func): The constraint to use for the coefficients.
      bias_initializer (str or func): The initialiser to use for the bias.
      bias_regularizer (str or func): The regulariser to use for the bias.
      bias_constraint (str or func): The constraint to use for the bias.
      input_dim (int, optional): the size of the input shape, if known.
      kwargs: any additional arguments to pass to :class:`tensorflow.keras.layers.Layer`
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docstr: Gets class configuration for Keras serialization.
  Used by Keras model serialization.
  
  Returns:
      A dictionary that contains the config of the layer
docstr_tok: ["Gets","class","configuration","for","Keras","serialization",".","Used","by","Keras","model","serialization",".","Returns",":","A","dictionary","that","contains","the","config","of","the","layer"]
code: def get_config(self):
      """
      Gets class configuration for Keras serialization.
      Used by Keras model serialization.
  
      Returns:
          A dictionary that contains the config of the layer
      """
  
      config = {
          "units": self.units,
          "use_bias": self.use_bias,
          "activation": activations.serialize(self.activation),
          "kernel_initializer": initializers.serialize(
              self.kernel_initializer
          ),
          "basis_initializer": initializers.serialize(
              self.basis_initializer
          ),
          "coefficient_initializer": initializers.serialize(
              self.coefficient_initializer
          ),
          "bias_initializer": initializers.serialize(
              self.bias_initializer
          ),
          "kernel_regularizer": regularizers.serialize(
              self.kernel_regularizer
          ),
          "basis_regularizer": regularizers.serialize(
              self.basis_regularizer
          ),
          "coefficient_regularizer": regularizers.serialize(
              self.coefficient_regularizer
          ),
          "bias_regularizer": regularizers.serialize(
              self.bias_regularizer
          ),
          "kernel_constraint": constraints.serialize(
              self.kernel_constraint
          ),
          "basis_constraint": constraints.serialize(
              self.basis_constraint
          ),
          "coefficient_constraint": constraints.serialize(
              self.coefficient_constraint
          ),
          "bias_constraint": constraints.serialize(
              self.bias_constraint
          ),
          "num_relationships": self.num_relationships,
          "num_bases": self.num_bases,
      }
  
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: stellargraph/stellargraph/layer/rgcn.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Layer, Lambda, Dropout, Input
  from tensorflow.keras import activations, initializers, constraints, regularizers
  from .misc import SqueezedSparseConversion, deprecated_model_function, GatherIndices
  from ..mapper.full_batch_generators import RelationalFullBatchNodeGenerator
label_desc: Use image node_id 2 for calling the RelationalGraphConvolution obj's underlying member method code with example usage: obj.get_config() and returns: dict