code: def test_read_file(self):
      with self.test_session() as sess:
          # Construct a queue containing a list of filenames.
          filename_queue = tf.train.string_input_producer(
              ["tests/data/toy_data.tfrecords"]
          )
          # Read a single serialized example from a filename.
          # `serialized_example` is a Tensor of type str.
          reader = tf.TFRecordReader()
          _, serialized_example = reader.read(filename_queue)
          # Convert serialized example back to actual values,
          # describing format of the objects to be returned.
          features = tf.parse_single_example(
              serialized_example,
              features={"outcome": tf.FixedLenFeature([], tf.float32)},
          )
          x_batch = features["outcome"]
  
          mu = Normal(loc=0.0, scale=1.0)
          x = Normal(loc=tf.ones([]) * mu, scale=tf.ones([]))
  
          qmu = Normal(loc=tf.Variable(0.0), scale=tf.constant(1.0))
  
          inference = ed.KLqp({mu: qmu}, data={x: x_batch})
          inference.initialize(scale={x: 10.0})
  
          tf.global_variables_initializer().run()
  
          coord = tf.train.Coordinator()
          threads = tf.train.start_queue_runners(coord=coord)
  
          # Check data varies by session run.
          val = sess.run(inference.data[x])
          val_1 = sess.run(inference.data[x])
          self.assertNotEqual(val, val_1)
  
          coord.request_stop()
          coord.join(threads)
  
file_path: edward/tests/inferences/inference_data_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import six
  import tensorflow
  from edward.models import Normal
label_desc: Use image node_id 3 for calling the test_inference_data_class obj's underlying member method code with example usage: obj.test_read_file() without return types