(self, inputs)
Given preprocessed inputs, generate class probabilities by using the
        model to perform inference.

        Given an iterable of examples or numpy array where the first
        dimension is the number of example, return a n_examples x
        n_classes array of class predictions

        Args:
            inputs: Iterable of examples (e.g., a numpy array whose first
                dimension is the batch size).

        Returns:
            Class probabilities for each input example, as a numpy array of
            shape (num_examples, num_classes).
Given preprocessed inputs, generate class probabilities by using the
        model to perform inference.
Given preprocessed inputs generate class probabilities by using the model to perform inference .
def predict(self, inputs):
        """Given preprocessed inputs, generate class probabilities by using the
        model to perform inference.

        Given an iterable of examples or numpy array where the first
        dimension is the number of example, return a n_examples x
        n_classes array of class predictions

        Args:
            inputs: Iterable of examples (e.g., a numpy array whose first
                dimension is the batch size).

        Returns:
            Class probabilities for each input example, as a numpy array of
            shape (num_examples, num_classes).

        """
        raise NotImplementedError
def predict ( self , inputs ) : raise NotImplementedError