from ray.rllib.utils.tf_run_builder import TFRunBuilder # create a new instance of TFRunBuilder builder = TFRunBuilder(session, [x, y], [z]) # create a new TF session session = builder.build_session() # create a tensor flow graph session.run(tf.global_variables_initializer()) # perform a TensorFlow operation z_value = builder.call(lambda sess: sess.run(z), True, feed_dict={x: 1, y: 2})In the example code above, we first create a new instance of TFRunBuilder, passing in two lists representing the inputs (x and y) and outputs (z) of a TensorFlow operation. We then use the builder object to create a new TensorFlow session and initialize our graph. Finally, we call the run method on the builder object to run the TensorFlow operation with the inputs x=1 and y=2, and retrieve the output value z_value. Overall, TFRunBuilder is a useful utility class for building and executing TensorFlow models in a distributed environment, as provided by Ray RLlib.