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: Label data points.
  The index should be relative to the pool, not the overall data.
  
  Args:
      index: one or many indices to label.
      value: The label value. If not provided, no modification
                              to the underlying dataset is done.
docstr_tok: ["Label","data","points",".","The","index","should","be","relative","to","the","pool",",","not","the","overall","data",".","Args",":","index",":","one","or","many","indices","to","label",".","value",":","The","label","value",".","If","not","provided",",","no","modification","to","the","underlying","dataset","is","done","."]
code: def label(
      self, index: Union[list, int], value: Optional[Any] = None
  ) -> None:
      """
      Label data points.
      The index should be relative to the pool, not the overall data.
  
      Args:
          index: one or many indices to label.
          value: The label value. If not provided, no modification
                                  to the underlying dataset is done.
      """
      raise NotImplementedError
  
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 8 for calling the SplittedDataset obj's underlying member method code with example usage: obj.label(index, value) without return types