class_docstr: Graph convolution layer.
class_docstr_tok: ["Graph","convolution","layer","."]
docstr: Run over inputs.
docstr_tok: ["Run","over","inputs","."]
code: def _call(self, inputs):
      """Run over inputs."""
      x = inputs
  
      # Dropout.
      if self.sparse_inputs:
          x = sparse_dropout(
              x, 1 - self.dropout, self.num_features_nonzero
          )
      else:
          x = tf.nn.dropout(x, 1 - self.dropout)
  
      # Convolve.
      if not self.featureless:
          pre_sup = dot(
              x, self.vars["weights"], sparse=self.sparse_inputs
          )
      else:
          pre_sup = self.vars["weights"]
      support = dot(self.support, pre_sup, sparse=True)
      output = support
  
      # Bias.
      if self.bias:
          output += self.vars["bias"]
  
      return self.act(output)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from .models_base import glorot
  from .models_base import Model
  import tensorflow
label_desc: Use image node_id 3 for calling the GraphConvolution obj's underlying member method code with example usage: obj._call(inputs) and returns: self