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
class_docstr_tok: ["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"]
code: def __iter__(self):
      return zip(*self._dataset)
  
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 5 for calling the ActiveNumpyArray obj's underlying member method code with example usage: obj.__iter__() and returns: zip