parameters: (self, x=None, input_fn=None, batch_size=None, outputs=None,
                axis=1)
return_statement: return result
docstring: Predict class or regression for `x`.
docstring_summary: Predict class or regression for `x`.
docstring_tokens: Predict class or regression for x .
function: def predict(self, x=None, input_fn=None, batch_size=None, outputs=None,
                axis=1):
      """Predict class or regression for `x`."""
      if x is not None:
        predict_data_feeder = setup_train_data_feeder(
            x, None, n_classes=None,
            batch_size=batch_size or self.batch_size,
            shuffle=False, epochs=1)
        result = super(DeprecatedMixin, self)._infer_model(
            input_fn=predict_data_feeder.input_builder,
            feed_fn=predict_data_feeder.get_feed_dict_fn(),
            outputs=outputs)
      else:
        result = super(DeprecatedMixin, self)._infer_model(
            input_fn=input_fn, outputs=outputs)
      if self.__deprecated_n_classes > 1 and axis is not None:
        return np.argmax(result, axis)
      return result
function_tokens: def predict ( self , x = None , input_fn = None , batch_size = None , outputs = None , axis = 1 ) : if x is not None : predict_data_feeder = setup_train_data_feeder ( x , None , n_classes = None , batch_size = batch_size or self . batch_size , shuffle = False , epochs = 1 ) result = super ( DeprecatedMixin , self ) . _infer_model ( input_fn = predict_data_feeder . input_builder , feed_fn = predict_data_feeder . get_feed_dict_fn ( ) , outputs = outputs ) else : result = super ( DeprecatedMixin , self ) . _infer_model ( input_fn = input_fn , outputs = outputs ) if self . __deprecated_n_classes > 1 and axis is not None : return np . argmax ( result , axis ) return result