code: def _test(self):
      X_train = np.zeros([500, 100])
      y_train = np.zeros(500)
  
      N = X_train.shape[0]  # number of data points
      D = X_train.shape[1]  # number of features
  
      W_1 = Normal(loc=tf.zeros([D, 20]), scale=tf.ones([D, 20]) * 100)
      W_2 = Normal(
          loc=tf.zeros([20, 15]), scale=tf.ones([20, 15]) * 100
      )
      W_3 = Normal(loc=tf.zeros([15, 1]), scale=tf.ones([15, 1]) * 100)
      b_1 = Normal(loc=tf.zeros(20), scale=tf.ones(20) * 100)
      b_2 = Normal(loc=tf.zeros(15), scale=tf.ones(15) * 100)
  
      X = tf.placeholder(tf.float32, [N, D])
      y = Bernoulli(logits=neural_network(X, W_1, W_2, W_3, b_1, b_2))
      return N, D, W_1, W_2, W_3, b_1, b_2, X, y, X_train, y_train
  
file_path: edward/tests/inferences/bayesian_nn_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import tensorflow
  from edward.models import Empirical, Bernoulli, Normal
label_desc: Use image node_id 1 for calling the test_inference_bayesian_nn_class obj's underlying member method code with example usage: obj._test() and returns: N,  D,  W_1,  W_2,  W_3,  b_1,  b_2,  X,  y,  X_train,  y_train