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: Returns the indices required for the active step.
  
  Returns the indices of the labelled items. Also takes into account self.last_active_step.
  
  Returns:
      List of the selected indices for training.
docstr_tok: ["Returns","the","indices","required","for","the","active","step",".","Returns","the","indices","of","the","labelled","items",".","Also","takes","into","account","self.last_active_step",".","Returns",":","List","of","the","selected","indices","for","training","."]
code: def get_indices_for_active_step(self) -> List[int]:
      """Returns the indices required for the active step.
  
      Returns the indices of the labelled items. Also takes into account self.last_active_step.
  
      Returns:
          List of the selected indices for training.
      """
      if (curr_al_step := self.current_al_step) != self._indices_cache[
          0
      ]:
          if self.last_active_steps == -1:
              min_labelled_step = 0
          else:
              min_labelled_step = max(
                  0, curr_al_step - self.last_active_steps
              )
  
          # we need to work with lists since arrow dataset is not compatible with np.int types!
          indices = [
              indx
              for indx, val in enumerate(self.labelled_map)
              if val > min_labelled_step
          ]
          self._indices_cache = (curr_al_step, indices)
      return self._indices_cache[1]
  
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 2 for calling the SplittedDataset obj's underlying member method code with example usage: obj.get_indices_for_active_step() and returns: self