code: def evaluate(CPDs, posterior_params):
      true_p_A, pred_p_A = get_true_pred_CPDs(
          CPDs["p_A"], posterior_params["a"]
      )
      true_p_B, pred_p_B = get_true_pred_CPDs(
          CPDs["p_B"], posterior_params["b"]
      )
      true_p_C, pred_p_C = get_true_pred_CPDs(
          CPDs["p_C"], posterior_params["c"]
      )
      print("\np_A = True")
      print("actual:   ", true_p_A)
      print("predicted:", pred_p_A)
      print("\np_B = True | A = False/True")
      print("actual:   ", true_p_B)
      print("predicted:", pred_p_B)
      print("\np_C = True | B = False/True")
      print("actual:   ", true_p_C)
      print("predicted:", pred_p_C)
  
file_path: pyro/examples/toy_mixture_model_discrete_enumeration.py
imports: import argparse
  import matplotlib.pyplot
  import numpy
  import torch
  from torch.distributions import constraints
  from torch.distributions.bernoulli import Bernoulli
  from torch.distributions.beta import Beta
  from tqdm import tqdm
  import pyro
  import pyro.distributions
  import pyro.infer
  import pyro.optim
  from pyro.ops.indexing import Vindex
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(CPDs, posterior_params) without return types