class_docstr: Abstract class for Dataset that can be splitted.
  
  Args:
      labelled: An array that acts as a mask which is greater than 1 for every
          data point that is labelled, and 0 for every data point that is not
          labelled.
      random_state: Set the random seed for label_randomly().
      last_active_steps: If specified, will iterate over the last active steps
                          instead of the full dataset. Useful when doing partial finetuning.
docstr: An array that acts as a boolean mask which is True for every
  data point that is labelled, and False for every data point that is not
  labelled.
docstr_tok: ["An","array","that","acts","as","a","boolean","mask","which","is","True","for","every","data","point","that","is","labelled",",","and","False","for","every","data","point","that","is","not","labelled","."]
code: def labelled(self):
      """An array that acts as a boolean mask which is True for every
      data point that is labelled, and False for every data point that is not
      labelled."""
      return self.labelled_map.astype(bool)
  
imports: import warnings
  from typing import Union, List, Optional, Any, TYPE_CHECKING, Protocol
  import numpy
  from sklearn.utils import check_random_state
  from torch.utils import data
label_desc: Use image node_id 12 for calling the SplittedDataset obj's underlying member method code with example usage: obj.labelled() and returns: self