code: def create_data():
      num_train_samples = 1000
      num_val_samples = 400
      num_test_samples = 400
      input_time_steps = 24
      input_feature_dim = 2
      output_time_steps = 1
      output_feature_dim = 2
  
      def get_x_y(num_samples):
          x = np.random.rand(
              num_samples, input_time_steps, input_feature_dim
          ).astype(np.float32)
          y = x[:, -output_time_steps:, :] * 2 + np.random.rand(
              num_samples, output_time_steps, output_feature_dim
          ).astype(np.float32)
          return x, y
  
      train_data = get_x_y(num_train_samples)
      val_data = get_x_y(num_val_samples)
      test_data = get_x_y(num_test_samples)
      return train_data, val_data, test_data
  
imports: import numpy
  import tempfile
  import os
  import torch
  from zoo.chronos.forecaster.lstm_forecaster import LSTMForecaster
  from zoo.orca import init_orca_context, stop_orca_context
  from unittest import TestCase
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: create_data() and returns: train_data,  val_data,  test_data, x,  y