docstr: lagged target series for teacher forcing
  decoder_input data is one timestamp ahead of y
  :param x: 3-d array in format of (sample_num, past_sequence_len, feature_num)
  :param y: 3-d array in format of (sample_num, future_sequence_len, target_col_num)
            Need to expand dimension if y is a 2-d array with one target col
  :return: 3-d array of decoder inputs
docstr_tok: ["lagged","target","series","for","teacher","forcing","decoder_input","data","is","one","timestamp","ahead","of","y",":","param","x",":","3-d","array","in","format","of","(","sample_num",",","past_sequence_len",",","feature_num",")",":","param","y",":","3-d","array","in","format","of","(","sample_num",",","future_sequence_len",",","target_col_num",")","Need","to","expand","dimension","if","y","is","a","2-d","array","with","one","target","col",":","return",":","3-d","array","of","decoder","inputs"]
code: def _get_decoder_inputs(self, x, y):
      """
      lagged target series for teacher forcing
      decoder_input data is one timestamp ahead of y
      :param x: 3-d array in format of (sample_num, past_sequence_len, feature_num)
      :param y: 3-d array in format of (sample_num, future_sequence_len, target_col_num)
                Need to expand dimension if y is a 2-d array with one target col
      :return: 3-d array of decoder inputs
      """
      decoder_input_data = np.zeros(y.shape)
      decoder_input_data[1:,] = y[:-1,]
      decoder_input_data[0, 0] = x[-1, -1, : self.target_col_num]
      decoder_input_data[0, 1:] = y[0, :-1]
  
      return decoder_input_data
  
imports: import pickle
  import numpy
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Input, LSTM, Dense
  import tensorflow.keras
  from zoo.orca.automl.model.abstract import BaseModel
  from zoo.orca.automl.metrics import Evaluator
label_desc: Use image node_id 6 for calling the LSTMSeq2Seq obj's underlying member method code with example usage: obj._get_decoder_inputs(x, y) and returns: decoder_input_data