class_docstr: Single graph convolution layer.
class_docstr_tok: ["Single","graph","convolution","layer","."]
docstr: Initializes the GraphConvLayer.
  
  Args:
    output_dim: (int) Output dimension of gcn layer
    bias: (bool) Whether bias needs to be added to the layer
    sparse: (bool) sparse: Whether features are sparse
    **kwargs: Keyword arguments for `tf.keras.layers.Layer`.
docstr_tok: ["Initializes","the","GraphConvLayer",".","Args",":","output_dim",":","(","int",")","Output","dimension","of","gcn","layer","bias",":","(","bool",")","Whether","bias","needs","to","be","added","to","the","layer","sparse",":","(","bool",")","sparse",":","Whether","features","are","sparse","*","*","kwargs",":","Keyword","arguments","for","`","tf.keras.layers.Layer","`","."]
code: def __init__(self, output_dim, bias, sparse=True, **kwargs):
      """Initializes the GraphConvLayer.
  
      Args:
        output_dim: (int) Output dimension of gcn layer
        bias: (bool) Whether bias needs to be added to the layer
        sparse: (bool) sparse: Whether features are sparse
        **kwargs: Keyword arguments for `tf.keras.layers.Layer`.
      """
      super(GraphConvLayer, self).__init__(**kwargs)
      self.output_dim = output_dim
      self.bias = bias
      self.sparse = sparse
  
imports: import tensorflow
label_desc: Use image node_id 1 to create a new GraphConvLayer object from inherited base classes: tf.keras.layers with example: obj = GraphConvLayer(output_dim, bias, sparse)