'Feature-extractor specific preprocessing.
See base class.
Args:
inputs: a [batch, height_in, width_in, channels] float tensor representing
a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: a [batch, height_out, width_out, channels] float
tensor representing a batch of images.
Raises:
ValueError: if inputs tensor does not have type tf.float32'

def preprocess(self, inputs):

    if (inputs.dtype is not tf.float32):
        raise ValueError('`preprocess`    expects    a    tf.float32    tensor')
    with tf.name_scope('Preprocessor'):
        resized_inputs = tf.map_fn(self._image_resizer_fn, elems=inputs, dtype=tf.float32)
        return self._feature_extractor.preprocess(resized_inputs)
