'Initializes the generator.
Args:
model: Object encapsulating a trained image-to-text model. Must have
methods feed_image() and inference_step(). For example, an instance of
InferenceWrapperBase.
vocab: A Vocabulary object.
beam_size: Beam size to use when generating captions.
max_caption_length: The maximum caption length before stopping the search.
length_normalization_factor: If != 0, a number x such that captions are
scored by logprob/length^x, rather than logprob. This changes the
relative scores of captions depending on their lengths. For example, if
x > 0 then longer captions will be favored.'

def __init__(self, model, vocab, beam_size=3, max_caption_length=20, length_normalization_factor=0.0):

    self.vocab = vocab
    self.model = model
    self.beam_size = beam_size
    self.max_caption_length = max_caption_length
    self.length_normalization_factor = length_normalization_factor
