code: def call(self, inp, target_seq=None, training=False):
      decoder_inputs = self.decoder_inputs(
          inp[:, -1, : self.output_feature_num]
      )
      states = self.encoder(inp, training=training)
      all_outputs = []
      for seq_len in range(self.future_seq_len):
          if self.teacher_forcing and target_seq is not None:
              decoder_inputs = target_seq[:, seq_len : seq_len + 1, :]
          dec_outputs = self.decoder(
              decoder_inputs, training=training, states=states
          )
          decoder_outputs = self.fc(dec_outputs)
          all_outputs.append(decoder_outputs)
      outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
      return outputs
  
imports: import numpy
  import tensorflow
  from tensorflow.keras import backend
  from bigdl.nano.tf.keras import Model
  from tensorflow.keras.layers import LSTM, Dense, Lambda, Reshape, Layer, Input
label_desc: Use image node_id 2 for calling the LSTMSeq2Seq obj's underlying member method code with example usage: obj.call(inp, target_seq, training) and returns: outputs