(self, tokens, log_probs, state, attn_dists, p_gens, coverage)
Hypothesis constructor.

    Args:
      tokens: List of integers. The ids of the tokens that form the summary so far.
      log_probs: List, same length as tokens, of floats, giving the log probabilities of the tokens so far.
      state: Current state of the decoder, a LSTMStateTuple.
      attn_dists: List, same length as tokens, of numpy arrays with shape (attn_length). These are the attention distributions so far.
      p_gens: List, same length as tokens, of floats, or None if not using pointer-generator model. The values of the generation probability so far.
      coverage: Numpy array of shape (attn_length), or None if not using coverage. The current coverage vector.
Hypothesis constructor.
Hypothesis constructor .
def __init__(self, tokens, log_probs, state, attn_dists, p_gens, coverage):
    """Hypothesis constructor.

    Args:
      tokens: List of integers. The ids of the tokens that form the summary so far.
      log_probs: List, same length as tokens, of floats, giving the log probabilities of the tokens so far.
      state: Current state of the decoder, a LSTMStateTuple.
      attn_dists: List, same length as tokens, of numpy arrays with shape (attn_length). These are the attention distributions so far.
      p_gens: List, same length as tokens, of floats, or None if not using pointer-generator model. The values of the generation probability so far.
      coverage: Numpy array of shape (attn_length), or None if not using coverage. The current coverage vector.
    """
    self.tokens = tokens
    self.log_probs = log_probs
    self.state = state
    self.attn_dists = attn_dists
    self.p_gens = p_gens
    self.coverage = coverage
def __init__ ( self , tokens , log_probs , state , attn_dists , p_gens , coverage ) : self . tokens = tokens self . log_probs = log_probs self . state = state self . attn_dists = attn_dists self . p_gens = p_gens self . coverage = coverage