message: Fix some outdated documentation.
diff: @@ -19,10 +19,11 @@ on the `XLA_CPU` or `XLA_GPU` TensorFlow devices. Placing operators directly on
  a TensorFlow XLA device forces the operator to run on that device and is mainly
  used for testing.
  -> Note: The XLA CPU backend produces fast single-threaded code (in most cases),
  -> but does not yet parallelize as well as the TensorFlow CPU backend. The XLA
  -> GPU backend is competitive with the standard TensorFlow implementation,
  -> sometimes faster, sometimes slower.
  +> Note: The XLA CPU backend supports intra-op parallelism (i.e. it can shard a
  +> single operation across multiple cores) but it does not support inter-op
  +> parallelism (i.e. it cannot execute independent operations concurrently across
  +> multiple cores). The XLA GPU backend is competitive with the standard
  +> TensorFlow implementation, sometimes faster, sometimes slower.
  ### Turning on JIT compilation
  @@ -55,8 +56,7 @@ sess = tf.Session(config=config)
  > Note: Turning on JIT at the session level will not result in operations being
  > compiled for the CPU. JIT compilation for CPU operations must be done via
  -> the manual method documented below. This decision was made due to the CPU
  -> backend being single-threaded.
  +> the manual method documented below.
  #### Manual
  