class_docstr: Sort by the highest entropy.
  
  Args:
      shuffle_prop (float): Amount of noise to put in the ranking. Helps with selection bias
          (default: 0.0).
      reduction (Union[str, callable]): function that aggregates the results (default: `none`).
class_docstr_tok: ["Sort","by","the","highest","entropy",".","Args",":","shuffle_prop","(","float",")",":","Amount","of","noise","to","put","in","the","ranking",".","Helps","with","selection","bias","(","default",":","0.0",")",".","reduction","(","Union","[","str",",","callable","]",")",":","function","that","aggregates","the","results","(","default",":","`","none","`",")","."]
code: def compute_score(self, predictions):
      return scipy.stats.entropy(np.swapaxes(predictions, 0, 1))
  
imports: import types
  import warnings
  from collections.abc import Sequence
  from functools import wraps
  from typing import List
  import numpy
  import scipy.stats
  import torch
  from scipy.special import xlogy
  from torch import Tensor
  from baal.utils.array_utils import to_prob
label_desc: Use image node_id 2 for calling the Entropy obj's underlying member method code with example usage: obj.compute_score(predictions) and returns: scipy