code: def _decode_sequence(self, input_seq, mc=False):
      encoder_model, decoder_model = self._build_inference(mc=mc)
      # Encode the input as state vectors.
      states_value = encoder_model.predict(input_seq)
  
      # Generate empty target sequence of length 1.
      target_seq = np.zeros((len(input_seq), 1, self.target_col_num))
  
      # Populate the first target sequence with end of encoding series value
      target_seq[:, 0] = input_seq[:, -1, : self.target_col_num]
  
      # Sampling loop for a batch of sequences - we will fill decoded_seq with predictions
      # (to simplify, here we assume a batch of size 1).
  
      decoded_seq = np.zeros(
          (len(input_seq), self.future_seq_len, self.target_col_num)
      )
  
      for i in range(self.future_seq_len):
          output, h, c = decoder_model.predict(
              [target_seq] + states_value
          )
  
          decoded_seq[:, i] = output[:, 0]
  
          # Update the target sequence (of length 1).
          target_seq = np.zeros(
              (len(input_seq), 1, self.target_col_num)
          )
          target_seq[:, 0] = output[:, 0]
  
          # Update states
          states_value = [h, c]
  
      return decoded_seq
  
imports: import pickle
  import numpy
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Input, LSTM, Dense
  import tensorflow.keras
  from bigdl.orca.automl.model.abstract import BaseModel
  from bigdl.orca.automl.metrics import Evaluator
label_desc: Use image node_id 5 for calling the LSTMSeq2Seq obj's underlying member method code with example usage: obj._decode_sequence(input_seq, mc) and returns: decoded_seq