code: def load_task(task_id):
      task = openml.tasks.get_task(task_id)
      X, y = task.get_X_and_y()
      train_indices, test_indices = task.get_train_test_split_indices()
      X_train = X[train_indices]
      y_train = y[train_indices]
      X_test = X[test_indices]
      y_test = y[test_indices]
      dataset = openml.datasets.get_dataset(task.dataset_id)
      _, _, cat, _ = dataset.get_data(target=task.target_name)
      name = dataset.name.lower()
      del _
      del dataset
      cat = {
          i: "categorical" if c else "numerical"
          for i, c in enumerate(cat)
      }
  
      if isinstance(task, openml.tasks.OpenMLClassificationTask):
          task_type = "classification"
      elif isinstance(task, openml.tasks.OpenMLRegressionTask):
          task_type = "regression"
      else:
          raise ValueError("Unknown task type")
  
      return X_train, y_train, X_test, y_test, cat, task_type, name
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_task(task_id) and returns: X_train,  y_train,  X_test,  y_test,  cat,  task_type,  name