new_contents: # get tasks from openml
  
  import openml
  import pandas as pd
  from pprint import pprint
  
  def read_csv(file):
      # reads the given csv file and returns a list containing all dataset ids used for experiment.
      dataset_ids = []
      with open(file) as f:
          for line in f:
              dataset_id, _ = line.split('/')[2].split('_')
              dataset_ids.append(int(dataset_id))
  
      return dataset_ids
  
  def get_tasks_ids(dataset_ids):
      # returns a list containing tasj_ids
      task_ids = []
  
      # Some datasets are deactivated. We remove them from the list.
      deactivated_list = openml.datasets.list_datasets(status='deactivated')
      deactivated_list = list(deactivated_list.keys())
      for idx in deactivated_list:
          if idx in dataset_ids:
              #print("datset id %i is deactivated. Removing from list" % idx)
              dataset_ids.remove(idx)
  
      # Get task ids.
      tasks = openml.tasks.list_tasks(task_type_id=1)
      tasks = pd.DataFrame.from_dict(tasks, orient="index")
  
      for did in dataset_ids:
          task = tasks.query('did == {}'.format(did))
          task = list(task.tid)
          task_ids.append(task[0])  # append first task id which has given dataset id.
  
      return task_ids
  
  def main():
      datasets = 'resources/datasets.csv'
      dataset_ids = read_csv(datasets)
      task_ids = get_tasks_ids(dataset_ids)
      string_to_print = ''
      for tid in task_ids:
          string_to_print += str(tid) + ' '
      print(string_to_print)
  
  
  
  if __name__=="__main__":
      tasks = openml.tasks.list_tasks(task_type_id=1)
      # For each tasks, if tasks.did in [dataset_ids]: remember the its task id.
  
      main()
  
  
  
subject: ADD get_tasks.py. converts given dataset_ids to task_ids.