docstr: Parameters
  ----------
  n_features
      Number of candidate anchors.
  t
      Iteration number.
  delta
      Confidence budget, candidate anchors have close to optimal precisions with prob. `1 - delta`.
  
  Returns
  -------
  Level used to update upper and lower precision bounds.
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code: def compute_beta(n_features: int, t: int, delta: float) -> float:
      """
      Parameters
      ----------
      n_features
          Number of candidate anchors.
      t
          Iteration number.
      delta
          Confidence budget, candidate anchors have close to optimal precisions with prob. `1 - delta`.
  
      Returns
      -------
      Level used to update upper and lower precision bounds.
      """
      # The following constants are defined and used in the paper introducing the KL-LUCB bandit algorithm
      # (http://proceedings.mlr.press/v30/Kaufmann13.html). Specifically, Theorem 1 proves the lower bounds
      # of these constants to ensure the algorithm is PAC with probability at least 1-delta. Also refer to
      # section "5. Numerical experiments" where these values are used empirically.
      alpha = 1.1
      k = 405.5
      temp = np.log(k * n_features * (t**alpha) / delta)
  
      return temp + np.log(temp)
  
imports: import copy
  import logging
  from collections import defaultdict, namedtuple
  from typing import Callable, Dict, List, Optional, Set, Tuple
  import numpy
  from alibi.utils.distributions import kl_bernoulli
label_desc: Use image node_id 6 for calling the AnchorBaseBeam obj's underlying member method code with example usage: obj.compute_beta(n_features, t, delta) and returns: unknown