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 `n` data-points randomly.
  
  Args:
      n (int): Number of samples to label.
docstr_tok: ["Label","`","n","`","data-points","randomly",".","Args",":","n","(","int",")",":","Number","of","samples","to","label","."]
code: def label_randomly(self, n: int = 1) -> None:
      """
      Label `n` data-points randomly.
  
      Args:
          n (int): Number of samples to label.
      """
      self.label(
          self.random_state.choice(
              self.n_unlabelled, n, replace=False
          ).tolist()
      )
  
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 9 for calling the SplittedDataset obj's underlying member method code with example usage: obj.label_randomly(n) without return types