code: def _build_inference(self, mc=False):
      training = True if mc else None
      # from our previous model - mapping encoder sequence to state vectors
      encoder_model = Model(self.encoder_inputs, self.encoder_states)
  
      # A modified version of the decoding stage that takes in predicted target inputs
      # and encoded state vectors, returning predicted target outputs and decoder state vectors.
      # We need to hang onto these state vectors to run the next step of the inference loop.
      decoder_state_input_h = Input(shape=(self.latent_dim,))
      decoder_state_input_c = Input(shape=(self.latent_dim,))
      decoder_states_inputs = [
          decoder_state_input_h,
          decoder_state_input_c,
      ]
  
      decoder_outputs, state_h, state_c = self.decoder_lstm(
          self.decoder_inputs,
          training=training,
          initial_state=decoder_states_inputs,
      )
      decoder_states = [state_h, state_c]
  
      decoder_outputs = self.decoder_dense(decoder_outputs)
      decoder_model = Model(
          [self.decoder_inputs] + decoder_states_inputs,
          [decoder_outputs] + decoder_states,
      )
      return encoder_model, decoder_model
  
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 4 for calling the LSTMSeq2Seq obj's underlying member method code with example usage: obj._build_inference(mc) and returns: encoder_model,  decoder_model