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","."]
code: def predict_on_dataset(
      self,
      dataset: Dataset,
      batch_size: int,
      iterations: int,
      use_cuda: bool,
      workers: int = 4,
      collate_fn: Optional[Callable] = None,
      half=False,
      verbose=True,
  ):
      return (
          super()
          .predict_on_dataset(
              dataset,
              batch_size,
              iterations,
              use_cuda,
              workers,
              collate_fn,
              half,
              verbose,
          )
          .reshape([-1])
      )
  
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 4 for calling the AbstractGPUHeuristic obj's underlying member method code with example usage: obj.predict_on_dataset(dataset, batch_size, iterations, use_cuda, workers, collate_fn, half, verbose) and returns: super