code: def test_data(self):
      with self.test_session():
          x_ph = tf.placeholder(tf.float32, [])
          x = Normal(loc=x_ph, scale=1.0)
          y = 2.0 * Normal(loc=0.0, scale=1.0)
          x_data = tf.constant(0.0)
          x_ph_data = np.array(0.0)
          y_data = tf.constant(20.0)
          ed.evaluate(
              "mean_squared_error",
              {x: x_data, x_ph: x_ph_data},
              n_samples=1,
          )
          ed.evaluate("mean_squared_error", {y: y_data}, n_samples=1)
          self.assertRaises(
              TypeError,
              ed.evaluate,
              "mean_squared_error",
              {"y": y_data},
              n_samples=1,
          )
  
file_path: edward/tests/criticisms/evaluate_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 Bernoulli, Categorical, Multinomial, Normal
label_desc: Use image node_id 4 for calling the test_evaluate_class obj's underlying member method code with example usage: obj.test_data() without return types