from ray.rllib.utils.tf_run_builder import TFRunBuilder # Create a new TFRunBuilder object builder = TFRunBuilder() # Build a TensorFlow session sess = builder.build_session() # Run a TensorFlow operation in the session result = sess.run(my_op) # Compute gradients of a TensorFlow operation grads = builder.compute_gradients(my_op) # Apply gradients to a TensorFlow model apply_op = builder.apply_gradients(optimizer, grads) # Close the TensorFlow session builder.close_session(sess)In the above code snippets, we demonstrate how to create a new TFRunBuilder object, build a TensorFlow session, run a TensorFlow operation, compute gradients of a TensorFlow operation, apply gradients to a TensorFlow model, and finally close the TensorFlow session. Overall, TFRunBuilder is a useful class for managing TensorFlow sessions and operations within the Ray RLlib library.