class_docstr: Graph convolution layer.
class_docstr_tok: ["Graph","convolution","layer","."]
code: def _call(self, 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
      supports = list()
      for i in range(len(self.support)):
          if not self.featureless:
              pre_sup = dot(
                  x,
                  self.vars["weights_" + str(i)],
                  sparse=self.sparse_inputs,
              )
          else:
              pre_sup = self.vars["weights_" + str(i)]
          support = dot(self.support[i], pre_sup, sparse=True)
          supports.append(support)
      output = tf.add_n(supports)
  
      # bias
      if self.bias:
          output += self.vars["bias"]
  
      return self.act(output)
  
file_path: OpenNE/src/openne/gcn/layers.py
imports: from .inits import *
  import tensorflow
label_desc: Use image node_id 2 for calling the GraphConvolution obj's underlying member method code with example usage: obj._call(inputs) and returns: self