code: def get_generated_data():
      # function generates simple training data that guarantee convergence
      # within 30 epochs for suitable config
  
      # generate data
      np.random.seed(RANDOM_SEED)
      x = np.array(range(NUMBER_OBSERVATIONS)).reshape(-1, 1)
      y = 2 * x + 1 + np.random.normal(size=x.shape[0]).reshape(-1, 1)
      raw_df = pd.DataFrame(
          np.concatenate((x, y), axis=1), columns=["x", "y"]
      )
  
      # create training data
      train, valid_test = train_test_split(raw_df, train_size=0.7)
  
      # create validation and test data
      validation, test = train_test_split(valid_test, train_size=0.5)
  
      return GeneratedData(train, validation, test)
  
imports: from collections import namedtuple
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 2 for calling a global function with example usage: get_generated_data() and returns: GeneratedData