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: The number of labelled data points.
docstr_tok: ["The","number","of","labelled","data","points","."]
code: def n_labelled(self):
      """The number of labelled data points."""
      return self.labelled.sum()
  
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 7 for calling the SplittedDataset obj's underlying member method code with example usage: obj.n_labelled() and returns: self