code: def ctc_greedy_decode(predict_seqs, blank_id, unique=True):
      """
      decod using simple greedy search
      :param predict_seqs: ([B, T, C], type=float) the output of CTC-based asr model.
      :param blank_id: (type=int) the index of blank symbol
      :param unique: (type=boolean) if merge consecutive repeated classes in output
      :return: ([B, T]) the result of greedy decode
      """
      decode_seq = []
  
      for predict_seq in predict_seqs:
          cur_token_list = [
              np.argmax(prob_list) for prob_list in predict_seq
          ]
  
          cur_decode_list = []
          if unique:
              temp = groupby(cur_token_list)
              cur_decode_list = [
                  key for key, group in temp if key != blank_id
              ]
          else:
              cur_decode_list = [
                  token_id
                  for token_id in cur_token_list
                  if token_id != blank_id
              ]
          decode_seq.append(cur_decode_list)
  
      return decode_seq
  
label_desc: Use image node_id 1 for calling a global function with example usage: ctc_greedy_decode(predict_seqs, blank_id, unique) and returns: decode_seq