parameters: (self, model_fn, n_classes, model_dir=None, config=None)
docstring: Constructor for Classifier.
  
      Args:
        model_fn: (targets, predictions, mode) -> logits, loss, train_op
        n_classes: Number of classes
        model_dir: Directory to save model parameters, graph and etc. This can also
          be used to load checkpoints from the directory into a estimator to continue
          training a previously saved model.
        config: Configuration object (optional)
docstring_summary: Constructor for Classifier.
docstring_tokens: Constructor for Classifier .
function: def __init__(self, model_fn, n_classes, model_dir=None, config=None):
      """Constructor for Classifier.
  
      Args:
        model_fn: (targets, predictions, mode) -> logits, loss, train_op
        n_classes: Number of classes
        model_dir: Directory to save model parameters, graph and etc. This can also
          be used to load checkpoints from the directory into a estimator to continue
          training a previously saved model.
        config: Configuration object (optional)
      """
      self._n_classes = n_classes
      self._logits_fn = model_fn
      super(Classifier, self).__init__(model_fn=self._classifier_model,
                                       model_dir=model_dir, config=config)
function_tokens: def __init__ ( self , model_fn , n_classes , model_dir = None , config = None ) : self . _n_classes = n_classes self . _logits_fn = model_fn super ( Classifier , self ) . __init__ ( model_fn = self . _classifier_model , model_dir = model_dir , config = config )