code: def main():
      """
      Download example data and split it into training and test data.
  
      The california data set is meant for regression modeling.
      """
      print("Retrieving california data from servers...", end="")
      california = sklearn.datasets.fetch_california_housing()
      print("done")
  
      # this dataset contains 20,640 samples which is too many
      # let's just sample this dataset to get 500 samples
      rng = np.random.default_rng(42)
      chosen_indices = rng.integers(
          0, california.target.shape[0], size=500
      )
      X = california.data[chosen_indices, :]
      Y = california.target[chosen_indices]
  
      # create example jsonlines dictionaries
      examples = [
          {
              "id": f"EXAMPLE_{i}",
              "y": y,
              "x": {f"f{j}": x_val for j, x_val in enumerate(x)},
          }
          for i, (x, y) in enumerate(zip(X, Y))
      ]
  
      (examples_train, examples_test) = train_test_split(
          examples, test_size=0.33, random_state=42
      )
  
      print("Writing training and testing files...", end="")
      for examples, suffix in [
          (examples_train, "train"),
          (examples_test, "test"),
      ]:
          california_dir = Path("california") / suffix
          if not california_dir.exists():
              california_dir.mkdir(parents=True)
          jsonlines_path = (
              california_dir / "example_california_features.jsonlines"
          )
          with open(jsonlines_path, "w") as f:
              for ex in examples:
                  f.write(f"{json.dumps(ex)}\n")
      print("done")
  
imports: import json
  from pathlib import Path
  import numpy
  import sklearn.datasets
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types