code: def compute_wilson_bounds(
      p, n, digits=digits_of_precision, z=z_score
  ):
      """Returns lower and upper bound (Binomial Proportion)"""
      denominator = 1 + z**2 / n
      centre_adjusted_probability = p + z * z / (2 * n)
      adjusted_standard_deviation = np.sqrt(
          (p * (1 - p) + z * z / (4 * n))
      ) / np.sqrt(n)
      lower_bound = (
          centre_adjusted_probability - z * adjusted_standard_deviation
      ) / denominator
      upper_bound = (
          centre_adjusted_probability + z * adjusted_standard_deviation
      ) / denominator
      return (round(lower_bound, digits), round(upper_bound, digits))
  
file_path: responsible-ai-toolbox/raiwidgets/raiwidgets/fairness_metric_calculation.py
imports: import importlib
  import numpy
  from packaging import version
  from sklearn.metrics import confusion_matrix
label_desc: Use image node_id 1 for calling a global function with example usage: compute_wilson_bounds(p, n, digits, z) and returns: round,  round