code: def create_next(
      self,
      token,
      log_prob,
      dec_state,
      input_feed,
      non_word,
      add_enc_attn_weights,
  ):
      if self.use_coverage:
          assert len(add_enc_attn_weights.shape) == 3
          assert add_enc_attn_weights.shape[0] == 1
          assert add_enc_attn_weights.shape[1] == 1
          enc_attn_weights_processed = [
              t.clone() for t in self.enc_attn_weights
          ]
          enc_attn_weights_processed.append(add_enc_attn_weights)
      else:
          enc_attn_weights_processed = []
      return Hypothesis(
          tokens=self.tokens + [token],
          log_probs=self.log_probs + [log_prob],
          dec_state=dec_state,
          input_feed=input_feed,
          num_non_words=self.num_non_words + 1
          if non_word
          else self.num_non_words,
          enc_attn_weights=enc_attn_weights_processed,
          use_coverage=self.use_coverage,
          states_for_tree=self.states_for_tree + [dec_state]
          if self.states_for_tree is not None
          else None,
      )
  
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 5 for calling the Hypothesis obj's underlying member method code with example usage: obj.create_next(token, log_prob, dec_state, input_feed, non_word, add_enc_attn_weights) and returns: Hypothesis