code: def test(self):
      with self.test_session():
          X_train = np.zeros([100, 10], dtype=np.float32)
          y_train = np.zeros(100, dtype=np.int32)
  
          N, D = X_train.shape
          H = 10  # number of hidden units
          K = 10  # number of classes
  
          W_0 = Normal(loc=tf.zeros([D, H]), scale=tf.ones([D, H]))
          W_1 = Normal(loc=tf.zeros([H, K]), scale=tf.ones([H, K]))
          b_0 = Normal(loc=tf.zeros(H), scale=tf.ones(H))
          b_1 = Normal(loc=tf.zeros(K), scale=tf.ones(K))
  
          y = Categorical(
              logits=neural_network(X_train, W_0, W_1, b_0, b_1)
          )
  
          qW_0 = Normal(
              loc=tf.Variable(tf.random_normal([D, H])),
              scale=tf.nn.softplus(
                  tf.Variable(tf.random_normal([D, H]))
              ),
          )
          qW_1 = Normal(
              loc=tf.Variable(tf.random_normal([H, K])),
              scale=tf.nn.softplus(
                  tf.Variable(tf.random_normal([H, K]))
              ),
          )
          qb_0 = Normal(
              loc=tf.Variable(tf.random_normal([H])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([H]))),
          )
          qb_1 = Normal(
              loc=tf.Variable(tf.random_normal([K])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([K]))),
          )
  
          inference = ed.KLqp(
              {W_0: qW_0, b_0: qb_0, W_1: qW_1, b_1: qb_1},
              data={y: y_train},
          )
          inference.run(n_iter=1)
  
file_path: edward/tests/inferences/inference_integer_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 Normal, Categorical
label_desc: Use image node_id 1 for calling the test_integer_init obj's underlying member method code with example usage: obj.test() without return types