class_docstr: An abstract interface for model wrappers that exposes model symbols
  needed for making an attack. This abstraction removes the dependency on
  any specific neural network package (e.g. Keras) from the core
  code of CleverHans. It can also simplify exposing the hidden features of a
  model when a specific package does not directly expose them.
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docstr: :param x: A symbolic representation (Tensor) of the network input
  :return: A symbolic representation (Tensor) of the predicted label
docstr_tok: [":","param","x",":","A","symbolic","representation","(","Tensor",")","of","the","network","input",":","return",":","A","symbolic","representation","(","Tensor",")","of","the","predicted","label"]
code: def get_predicted_class(self, x, **kwargs):
      """
      :param x: A symbolic representation (Tensor) of the network input
      :return: A symbolic representation (Tensor) of the predicted label
      """
      return tf.argmax(self.get_logits(x, **kwargs), axis=1)
  
imports: from abc import ABCMeta
  import warnings
  import tensorflow
  from cleverhans import utils_tf
label_desc: Use image node_id 4 for calling the Model obj's underlying member method code with example usage: obj.get_predicted_class(x) and returns: tf