class_docstr: Abstract class that defines a Heuristic.
  
  Args:
      shuffle_prop (float): shuffle proportion.
      threshold (Optional[float]): threshold the probabilities.
      reverse (bool): True if the most uncertain sample has the highest value.
      reduction (Union[str, Callable]): Reduction used after computing the score.
class_docstr_tok: ["Abstract","class","that","defines","a","Heuristic",".","Args",":","shuffle_prop","(","float",")",":","shuffle","proportion",".","threshold","(","Optional","[","float","]",")",":","threshold","the","probabilities",".","reverse","(","bool",")",":","True","if","the","most","uncertain","sample","has","the","highest","value",".","reduction","(","Union","[","str",",","Callable","]",")",":","Reduction","used","after","computing","the","score","."]
docstr: Compute the score according to the heuristic.
  Args:
      predictions (ndarray): Array of predictions
  
  Returns:
      Array of scores.
docstr_tok: ["Compute","the","score","according","to","the","heuristic",".","Args",":","predictions","(","ndarray",")",":","Array","of","predictions","Returns",":","Array","of","scores","."]
code: def compute_score(self, predictions):
      """
      Compute the score according to the heuristic.
      Args:
          predictions (ndarray): Array of predictions
  
      Returns:
          Array of scores.
      """
      raise NotImplementedError
  
file_path: baal/baal/active/heuristics/heuristics_gpu.py
imports: from typing import Callable, Optional, Sequence
  import numpy
  import torch
  import torch.nn.functional
  from baal.active.dataset.base import Dataset
  from baal import ModelWrapper
label_desc: Use image node_id 2 for calling the AbstractGPUHeuristic obj's underlying member method code with example usage: obj.compute_score(predictions) without return types