code: def create_data(self):
      def gen_train_sample(data, past_seq_len, future_seq_len):
          data = pd.DataFrame(data)
          x, y = self.ft._roll_train(
              data,
              past_seq_len=past_seq_len,
              future_seq_len=future_seq_len,
          )
          return x, y
  
      def gen_test_sample(data, past_seq_len):
          test_data = pd.DataFrame(data)
          x = self.ft._roll_test(test_data, past_seq_len=past_seq_len)
          return x
  
      self.long_num = 6
      self.time_step = 2
      look_back = (self.long_num + 1) * self.time_step
      look_forward = 1
      self.x_train, self.y_train = gen_train_sample(
          data=np.random.randn(64, 4),
          past_seq_len=look_back,
          future_seq_len=look_forward,
      )
      self.x_val, self.y_val = gen_train_sample(
          data=np.random.randn(16, 4),
          past_seq_len=look_back,
          future_seq_len=look_forward,
      )
      self.x_test = gen_test_sample(
          data=np.random.randn(16, 4), past_seq_len=look_back
      )
  
file_path: analytics-zoo/pyzoo/test/zoo/chronos/forecaster/test_mtnet_forecaster.py
imports: import pytest
  import numpy
  from zoo.chronos.autots.deprecated.feature.time_sequence import TimeSequenceFeatureTransformer
  import tensorflow
  import pandas
  from zoo.chronos.forecaster.mtnet_forecaster import MTNetForecaster
  from unittest import TestCase
label_desc: Use image node_id 3 for calling the TestChronosModelMTNetForecaster obj's underlying member method code with example usage: obj.create_data() and returns: x,  y, x