old_contents: import numpy as np
  import openml
  
  
  classification_tasks = [
      233, 236, 242, 244, 246, 75090, 248, 251, 75124, 253, 254, 75092, 258,
      75093, 260, 261, 262, 75095, 266, 3043, 75097, 75098, 75099, 75159,
      75100, 275, 288, 75103, 75105, 75106, 75107, 75108, 75109, 75110,
      75112, 75113, 75114, 75115, 75116, 75117, 75119, 75120, 75121, 75123,
      252, 75125, 75126, 75127, 75129, 2117, 2119, 2120, 2122, 2123,
      75096, 75132, 75133, 75139, 75141, 75142, 75143, 75146, 75148, 75150,
      75153, 75154, 75230, 75156, 75157, 273, 2350, 75197, 75163, 75166,
      75169, 75171, 75172, 75173, 75174, 75175, 75176, 75177, 75179, 75240,
      75184, 75185, 75189, 75191, 75192, 75193, 75195, 75196, 75234, 75161,
      75198, 75168, 75201, 75202, 75203, 75188, 75205, 75207, 75210, 75212,
      75213, 75215, 75217, 75219, 75221, 75222, 75223, 75134, 75225, 75226,
      75227, 75231, 75232, 75233, 75101, 75235, 75236, 75237, 75178, 75239,
      75181, 75187, 75250, 75249, 75248, 75243, 75244, 75182]
  regression_tasks = [2280, 2288, 2289, 2292, 2300, 2306, 2307, 2309, 2313,
                      2315, 4768, 4769, 4772, 4774, 4779, 4790, 4796, 4835,
                      4881, 4883, 4885, 4892, 4893, 5022, 5024, 7393]
  
  
  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)
      del _
      del dataset
      cat = ['categorical' if c else 'numerical' for c in 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
  
new_contents: import numpy as np
  import openml
  
  
  classification_tasks = [
      233, 236, 242, 244, 246, 75090, 248, 251, 75124, 253, 254, 75092, 258,
      75093, 260, 261, 262, 75095, 266, 3043, 75097, 75098, 75099, 75159,
      75100, 275, 288, 75103, 75105, 75106, 75107, 75108, 75109, 75110,
      75112, 75113, 75114, 75115, 75116, 75117, 75119, 75120, 75121, 75123,
      252, 75125, 75126, 75127, 75129, 2117, 2119, 2120, 2122, 2123,
      75096, 75132, 75133, 75139, 75141, 75142, 75143, 75146, 75148, 75150,
      75153, 75154, 75230, 75156, 75157, 273, 2350, 75197, 75163, 75166,
      75169, 75171, 75172, 75173, 75174, 75175, 75176, 75177, 75179, 75240,
      75184, 75185, 75189, 75191, 75192, 75193, 75195, 75196, 75234, 75161,
      75198, 75168, 75201, 75202, 75203, 75188, 75205, 75207, 75210, 75212,
      75213, 75215, 75217, 75219, 75221, 75222, 75223, 75134, 75225, 75226,
      75227, 75231, 75232, 75233, 75101, 75235, 75236, 75237, 75178, 75239,
      75181, 75187, 75250, 75249, 75248, 75243, 75244, 75182]
  regression_tasks = [2280, 2288, 2289, 2292, 2300, 2306, 2307, 2309, 2313,
                      2315, 4768, 4769, 4772, 4774, 4779, 4790, 4796, 4835,
                      4881, 4883, 4885, 4892, 4893, 5022, 5024, ]
  
  
  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)
      del _
      del dataset
      cat = ['categorical' if c else 'numerical' for c in 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
  
subject: Remove dataset from regression meta-data