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: Converts predictions to probabilities.
  
  Args:
    predictions: A tensor of logits. For multiclass classification its shape
      is (num_samples, num_classes), where the second dimension contains a
      logit per class. For binary classification, its shape is (num_samples,),
      where each element is the probability of class 1 for that sample.
  
  Returns:
    A tensor of the same shape as predictions, with values between [0, 1]
  representing probabilities.
docstr_tok: ["Converts","predictions","to","probabilities",".","Args",":","predictions",":","A","tensor","of","logits",".","For","multiclass","classification","its","shape","is","(","num_samples",",","num_classes",")",",","where","the","second","dimension","contains","a","logit","per","class",".","For","binary","classification",",","its","shape","is","(","num_samples",",",")",",","where","each","element","is","the","probability","of","class","1","for","that","sample",".","Returns",":","A","tensor","of","the","same","shape","as","predictions",",","with","values","between","[","0",",","1","]","representing","probabilities","."]
code: def normalize_predictions(self, predictions):
      """Converts predictions to probabilities.
  
      Args:
        predictions: A tensor of logits. For multiclass classification its shape
          is (num_samples, num_classes), where the second dimension contains a
          logit per class. For binary classification, its shape is (num_samples,),
          where each element is the probability of class 1 for that sample.
  
      Returns:
        A tensor of the same shape as predictions, with values between [0, 1]
      representing probabilities.
      """
      if self.is_binary_classification:
          return tf.nn.sigmoid(predictions)
      return tf.nn.softmax(predictions, axis=-1)
  
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 6 for calling the GCN obj's underlying member method code with example usage: obj.normalize_predictions(predictions) and returns: tf, tf