class_docstr: Convenience class to let singletask models be fit on multitask data.
  
  This wrapper class groups a set of singletask `SklearnModel` objects to
  create a multitask model. This class exists primarily to facilitate
  benchmarking.
  
  Note
  ----
  This current implementation is only functional for sklearn models.
class_docstr_tok: ["Convenience","class","to","let","singletask","models","be","fit","on","multitask","data",".","This","wrapper","class","groups","a","set","of","singletask","`","SklearnModel","`","objects","to","create","a","multitask","model",".","This","class","exists","primarily","to","facilitate","benchmarking",".","Note","--","--","This","current","implementation","is","only","functional","for","sklearn","models","."]
docstr: Make directories to hold data for tasks
docstr_tok: ["Make","directories","to","hold","data","for","tasks"]
code: def _create_task_datasets(self, dataset):
      """Make directories to hold data for tasks"""
      task_data_dirs = []
      for task in self.tasks:
          task_data_dir = os.path.join(
              self.model_dir, str(task) + "_data"
          )
          if os.path.exists(task_data_dir):
              shutil.rmtree(task_data_dir)
          os.makedirs(task_data_dir)
          task_data_dirs.append(task_data_dir)
      task_datasets = self._to_singletask(dataset, task_data_dirs)
      for task, task_dataset in zip(self.tasks, task_datasets):
          logger.info(
              "Dataset for task %s has shape %s"
              % (task, str(task_dataset.get_shape()))
          )
      return task_datasets
  
file_path: deepchem/deepchem/models/multitask.py
imports: import os
  import numpy
  import shutil
  import logging
  from deepchem.models import Model
  from deepchem.data import DiskDataset
  from deepchem.trans import undo_transforms
label_desc: Use image node_id 2 for calling the SingletaskToMultitask obj's underlying member method code with example usage: obj._create_task_datasets(dataset) and returns: task_datasets