parameters: (self, x, out_lens, h_rnns,label,hidden, quantizer=None)
return_statement: return output,h_prim_rnns,hidden_predict,decode_count_batch
docstring: Returns a list of sentences given an input batch.
  
          Args:
              x: A tensor of size (seq_len, batch, in_features).
              out_lens: list of int representing the length of each sequence
                  output sequence.
              h_rnns: primier hidden of encoder Pre & Post  (hidden_pre,hidden_post)
              label: total label
              hidden: primier hidden of prediction net
          Returns:
              list containing batch number of sentences (strings).
docstring_summary: Returns a list of sentences given an input batch.
docstring_tokens: Returns a list of sentences given an input batch .
function: def decode(self, x, out_lens, h_rnns,label,hidden, quantizer=None):
          """Returns a list of sentences given an input batch.
  
          Args:
              x: A tensor of size (seq_len, batch, in_features).
              out_lens: list of int representing the length of each sequence
                  output sequence.
              h_rnns: primier hidden of encoder Pre & Post  (hidden_pre,hidden_post)
              label: total label
              hidden: primier hidden of prediction net
          Returns:
              list containing batch number of sentences (strings).
          """
          decode_count_batch = []
          with torch.no_grad():
              # Apply optional preprocessing
              #x_packed = torch.nn.utils.rnn.pack_padded_sequence(x, out_lens)
              x_packed = torch.nn.utils.rnn.pack_padded_sequence(x, out_lens)
  
              logits, out_lens, h_prim_rnns = self._model.encode(x_packed,h_rnns)
  
              output = []
              for batch_idx in range(logits.size(0)):
                  #wrong with batch32, label not [] for the post 31 data,should be []
                  label = []
                  inseq = logits[batch_idx, :, :].unsqueeze(1)
                  logitlen = out_lens[batch_idx]
                  sentence, hidden_predict, decode_count= self._greedy_decode(inseq, logitlen,label,hidden,quantizer=quantizer)
                  print()
                  decode_count_batch.append(decode_count)
                  output.append(sentence)
  
          return output,h_prim_rnns,hidden_predict,decode_count_batch
function_tokens: def decode ( self , x , out_lens , h_rnns , label , hidden , quantizer = None ) : decode_count_batch = [ ] with torch . no_grad ( ) : # Apply optional preprocessing #x_packed = torch.nn.utils.rnn.pack_padded_sequence(x, out_lens) x_packed = torch . nn . utils . rnn . pack_padded_sequence ( x , out_lens ) logits , out_lens , h_prim_rnns = self . _model . encode ( x_packed , h_rnns ) output = [ ] for batch_idx in range ( logits . size ( 0 ) ) : #wrong with batch32, label not [] for the post 31 data,should be [] label = [ ] inseq = logits [ batch_idx , : , : ] . unsqueeze ( 1 ) logitlen = out_lens [ batch_idx ] sentence , hidden_predict , decode_count = self . _greedy_decode ( inseq , logitlen , label , hidden , quantizer = quantizer ) print ( ) decode_count_batch . append ( decode_count ) output . append ( sentence ) return output , h_prim_rnns , hidden_predict , decode_count_batch