code: def __init__(
      self,
      tokens,
      log_probs,
      dec_state,
      input_feed,
      num_non_words,
      enc_attn_weights,
      use_coverage,
      states_for_tree=None,
  ):
      self.tokens = tokens
      self.states_for_tree = states_for_tree  # to record decoder hidden states to be sent to descendant decoding process. # noqa
      self.log_probs = log_probs
      self.dec_state = dec_state
      self.num_non_words = num_non_words
      self.enc_attn_weights = enc_attn_weights
      self.use_coverage = use_coverage
      self.input_feed = input_feed
      if not self.use_coverage:
          self.enc_attn_weights = []
  
imports: from copy import deepcopy
  import torch
  import torch.nn
  from graph4nlp.pytorch.modules.prediction.generation.base import DecoderBase
label_desc: Use image node_id 1 to create a new Hypothesis object from inherited base classes: object with example: obj = Hypothesis(tokens, log_probs, dec_state, input_feed, num_non_words, enc_attn_weights, use_coverage, states_for_tree)