code: def test_auto_transform_false(self):
      with self.test_session():
          # Match normal || softplus-inverse-normal distribution without
          # automated transformation; it should fail.
          x = TransformedDistribution(
              distribution=Normal(0.0, 0.5),
              bijector=tf.contrib.distributions.bijectors.Softplus(),
          )
          x.support = "nonnegative"
          qx = Normal(
              loc=tf.Variable(tf.random_normal([])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))),
          )
  
          inference = ed.KLqp({x: qx})
          inference.initialize(
              auto_transform=False, n_samples=5, n_iter=150
          )
          tf.global_variables_initializer().run()
          for _ in range(inference.n_iter):
              info_dict = inference.update()
  
          self.assertAllEqual(info_dict["loss"], np.nan)
  
file_path: edward/tests/inferences/inference_auto_transform_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, Gamma, Normal, PointMass, TransformedDistribution, Beta, Bernoulli
  from edward.util import transform
  from tensorflow.contrib.distributions import bijectors
label_desc: Use image node_id 2 for calling the test_inference_auto_transform_class obj's underlying member method code with example usage: obj.test_auto_transform_false() without return types