class_docstr: Implementation of BatchBALD from https://github.com/BlackHC/BatchBALD
  
  Args:
      num_samples (int): Number of samples to select (also called query_size).
      num_draw (int): Number of draw to perform from the history.
                      From the paper `40000 // num_classes` is suggested.
      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').
  
  Notes:
      This implementation returns the scores
       which is not necessarily ordered in the same way as they were selected.
  
  References:
      https://arxiv.org/abs/1906.08158
  
  Notes:
      K = iterations, C=classes
      Not tested on 4+ dims.
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code: def _conditional_entropy(self, probs):
      K = probs.shape[-1]
      return np.sum(-xlogy(probs, probs), axis=(1, -1)) / K
  
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 4 for calling the BatchBALD obj's underlying member method code with example usage: obj._conditional_entropy(probs) and returns: unknown