code: def test_fit_and_predict(self):
      seed = 1709
  
      df = pd.read_csv(
          "./tests/data/housing_regression_missing_values_missing_target.csv"
      )
      print(df.columns)
      x_cols = [c for c in df.columns if c != "MEDV"]
      X = df[x_cols]
      y = df["MEDV"]
  
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = sklearn.model_selection.train_test_split(
          X, y, test_size=0.3, random_state=seed
      )
      automl = AutoML(
          total_time_limit=10,
          algorithms=[
              "Xgboost"
          ],  # ["LightGBM", "RF", "NN", "CatBoost", "Xgboost"],
          start_random_models=1,
          hill_climbing_steps=0,
          top_models_to_improve=0,
          train_ensemble=True,
          verbose=True,
      )
      automl.fit(X_train, y_train)
  
      response = automl.predict(X_test)  # ["p_1"]
      print("Response", response)
  
file_path: mljar-supervised/tests/checks/check_automl_with_regression.py
imports: import unittest
  import tempfile
  import json
  import numpy
  import pandas
  from numpy.testing import assert_almost_equal
  from sklearn import datasets
  from supervised.automl import AutoML
  from supervised.metric import Metric
  import sklearn.model_selection
  from sklearn.metrics import log_loss
label_desc: Use image node_id 1 for calling the AutoMLWithRegressionTest obj's underlying member method code with example usage: obj.test_fit_and_predict() without return types