class_docstr: Graph Convolution Networks.
  
  Attributes:
    input_dim: Integer representing the number of input features.
    output_dim: Integer representing the number of classes.
    hidden: Integer representing the number of hidden units in the first layer
      of the network.
    dropout: Float representing the dropout probability during training.
    aggregation: String representing an aggregation operation, that is applied
      on the two inputs of the agreement model, after they are encoded through
      the convolution layers. See superclass attributes for details.
    activation: An activation function to be applied to the outputs of each
      fully connected layer of the aggregation network.
    is_binary_classification: Boolean specifying if this is model for binary
      classification. If so, it uses a different loss function and returns
      predictions with a single dimension, batch size.
    name: String representing the model name.
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docstr: Creates the model hidden representations and prediction ops.
  
  For this model, the hidden representation is the last layer of the MLP,
  before the logit computation. The predictions are unnormalized logits.
  
  Args:
    inputs: A tensor containing the model inputs. The first dimension is the
      batch size.
    is_train: A boolean placeholder specifying if this is a training or
      testing setting.
    support: TODO(dattias, kvis-google): add.
    num_features_nonzero: Number of non-zero features.
    **unused_kwargs: Other unused keyword arguments.
  
  Returns:
    encoding: A tensor containing an encoded batch of samples. The first
      dimension corresponds to the batch size.
    all_vars: A dictionary mapping from variable name to TensorFlow op
      containing all variables used in this model.
    reg_params: A dictionary mapping from a variable name to a Tensor of
      parameters which will be used for regularization.
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code: def get_encoding_and_params(
      self,
      inputs,
      is_train,
      support,
      num_features_nonzero,
      **unused_kwargs,
  ):
      """Creates the model hidden representations and prediction ops.
  
      For this model, the hidden representation is the last layer of the MLP,
      before the logit computation. The predictions are unnormalized logits.
  
      Args:
        inputs: A tensor containing the model inputs. The first dimension is the
          batch size.
        is_train: A boolean placeholder specifying if this is a training or
          testing setting.
        support: TODO(dattias, kvis-google): add.
        num_features_nonzero: Number of non-zero features.
        **unused_kwargs: Other unused keyword arguments.
  
      Returns:
        encoding: A tensor containing an encoded batch of samples. The first
          dimension corresponds to the batch size.
        all_vars: A dictionary mapping from variable name to TensorFlow op
          containing all variables used in this model.
        reg_params: A dictionary mapping from a variable name to a Tensor of
          parameters which will be used for regularization.
      """
      # Build layers.
      with tf.variable_scope(self.name + "/encoding"):
          hidden, reg_params = self._construct_encoding(
              inputs, is_train, support, num_features_nonzero
          )
  
          # Store model variables for easy access.
          variables = tf.get_collection(
              tf.GraphKeys.GLOBAL_VARIABLES,
              scope=tf.get_default_graph().get_name_scope(),
          )
          all_vars = {var.name: var for var in variables}
  
      return hidden, all_vars, reg_params
  
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 2 for calling the GCN obj's underlying member method code with example usage: obj.get_encoding_and_params(inputs, is_train, support, num_features_nonzero) and returns: hidden,  all_vars,  reg_params