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: Get the uncertainties
docstr_tok: ["Get","the","uncertainties"]
code: def get_uncertainties(self, predictions):
      """Get the uncertainties"""
      scores = self.compute_score(predictions)
      scores = self.reduction(scores)
      scores[~torch.isfinite(scores)] = 0.0 if self.reversed else 10000
      return scores
  
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 3 for calling the AbstractGPUHeuristic obj's underlying member method code with example usage: obj.get_uncertainties(predictions) and returns: scores