class_docstr: Graph convolution layer.
class_docstr_tok: ["Graph","convolution","layer","."]
code: def __init__(
      self,
      input_dim,
      output_dim,
      support,
      num_features_nonzero,
      dropout=0.0,
      sparse_inputs=False,
      activation=tf.nn.relu,
      bias=False,
      featureless=False,
      name=None,
  ):
      if not name:
          layer = self.__class__.__name__.lower()
          name = layer + "_" + str(get_layer_uid(layer))
  
      self.name = name
      self.vars = {}
      self.dropout = dropout
      self.act = activation
      self.support = support
      self.sparse_inputs = sparse_inputs
      self.featureless = featureless
      self.bias = bias
  
      # Helper variable for sparse dropout.
      self.num_features_nonzero = num_features_nonzero
  
      with tf.variable_scope(self.name + "_vars"):
          self.vars["weights"] = tf.get_variable(
              name="weights",
              initializer=glorot([input_dim, output_dim]),
          )
          if self.bias:
              self.vars["bias"] = tf.get_variable(
                  name="bias", initializer=tf.zeros(shape=[output_dim])
              )
  
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 1 to create a new GraphConvolution object from inherited base classes: object with example: obj = GraphConvolution(input_dim, output_dim, support, num_features_nonzero, dropout, sparse_inputs, activation, bias, featureless, name)