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: Check if a datapoint is labelled.
docstr_tok: ["Check","if","a","datapoint","is","labelled","."]
code: def is_labelled(self, idx: int) -> bool:
      """Check if a datapoint is labelled."""
      return bool(self.labelled[idx].item() == 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 3 for calling the SplittedDataset obj's underlying member method code with example usage: obj.is_labelled(idx) and returns: bool