code: def create_tiny_forecasting_dataset():
      X_train = pd.DataFrame(
          {
              "time": [
                  pd.Timestamp("2023-08-18T06:00:00"),
                  pd.Timestamp("2023-08-18T07:00:00"),
                  pd.Timestamp("2023-08-18T08:00:00"),
                  pd.Timestamp("2023-08-18T09:00:00"),
              ],
              "id": [1, 1, 1, 1],
          }
      )
      X_test = pd.DataFrame(
          {
              "time": [
                  pd.Timestamp("2023-08-18T10:00:00"),
                  pd.Timestamp("2023-08-18T11:00:00"),
                  pd.Timestamp("2023-08-18T12:00:00"),
                  pd.Timestamp("2023-08-18T13:00:00"),
              ],
              "id": [1, 1, 1, 1],
          }
      )
      y_train = [0, 1, 2, 3]
      y_test = [4, 5, 6, 7]
      return X_train, X_test, y_train, y_test
  
imports: import json
  import random
  import ssl
  import numpy
  import pandas
  import pytest
  from dice_ml.utils import helpers
  from sklearn.model_selection import train_test_split
  from rai_test_utils.datasets.tabular import create_iris_data
  from raiutils.common.retries import retry_function
label_desc: Use image node_id 2 for calling a global function with example usage: create_tiny_forecasting_dataset() and returns: X_train,  X_test,  y_train,  y_test