class_docstr: Graph convolution layer.
class_docstr_tok: ["Graph","convolution","layer","."]
code: def __init__(
      self,
      input_dim,
      output_dim,
      placeholders,
      dropout=0.0,
      sparse_inputs=False,
      act=tf.nn.relu,
      bias=False,
      featureless=False,
      **kwargs,
  ):
      super(GraphConvolution, self).__init__(**kwargs)
  
      if dropout:
          self.dropout = placeholders["dropout"]
      else:
          self.dropout = 0.0
  
      self.act = act
      self.support = placeholders["support"]
      self.sparse_inputs = sparse_inputs
      self.featureless = featureless
      self.bias = bias
  
      # helper variable for sparse dropout
      self.num_features_nonzero = placeholders["num_features_nonzero"]
  
      with tf.variable_scope(self.name + "_vars"):
          for i in range(len(self.support)):
              self.vars["weights_" + str(i)] = glorot(
                  [input_dim, output_dim], name="weights_" + str(i)
              )
          if self.bias:
              self.vars["bias"] = zeros([output_dim], name="bias")
  
      if self.logging:
          self._log_vars()
  
file_path: OpenNE/src/openne/gcn/layers.py
imports: from .inits import *
  import tensorflow
label_desc: Use image node_id 1 to create a new GraphConvolution object from inherited base classes: Layer with example: obj = GraphConvolution(input_dim, output_dim, placeholders, dropout, sparse_inputs, act, bias, featureless)