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: Rank the predictions according to their uncertainties.
docstr_tok: ["Rank","the","predictions","according","to","their","uncertainties","."]
code: def predict_on_batch(self, data, iterations=1, use_cuda=False):
      """Rank the predictions according to their uncertainties."""
      return self.get_uncertainties(
          self.model.predict_on_batch(data, iterations, cuda=use_cuda)
      )
  
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 5 for calling the AbstractGPUHeuristic obj's underlying member method code with example usage: obj.predict_on_batch(data, iterations, use_cuda) and returns: self