class_docstr: Active dataset for numpy arrays. Useful when using sklearn.
  
  Args:
      dataset (Tuple[ndarray, ndarray]): [Train x, train y], The dataset.
      labelled (Union[np.ndarray, torch.Tensor]):
          An array/tensor that acts as a boolean mask which is True for every
          data point that is labelled, and False for every data point that is not
          labelled.
      random_state: Random seed for the ActiveLearningDataset
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docstr: Label data points.
  The index should be relative to the pool, not the overall data.
  
  Args:
      index (Union[list,int]): one or many indices to label.
      value (Optional[Any]): The label value. If not provided, no modification
                              to the underlying dataset is done.
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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 (Union[list,int]): one or many indices to label.
          value (Optional[Any]): The label value. If not provided, no modification
                                  to the underlying dataset is done.
      """
      if isinstance(index, int):
          index = [index]
      if not isinstance(value, (list, tuple)):
          value = [value]
      indexes = self._pool_to_oracle_index(index)
      active_step = self.current_al_step + 1
      for index, val in zip_longest(indexes, value, fillvalue=None):
          self.labelled_map[index] = active_step
  
file_path: baal/baal/active/dataset/numpy.py
imports: from itertools import zip_longest
  from typing import Tuple, Optional, Any, Union
  import numpy
  from baal.active.dataset.base import SplittedDataset
label_desc: Use image node_id 7 for calling the ActiveNumpyArray obj's underlying member method code with example usage: obj.label(index, value) without return types