message: [Docs]: Reduce over-estimation while measuring compute time.
  Inspired by:
diff: @@ -727,7 +727,13 @@ def measure(x, steps):
  start = time.time()
  for i in range(steps):
  x = tf.matmul(x, x)
  - _ = x.numpy() # Make sure to execute op and not just enqueue it
  + # tf.matmul can return before completing the matrix multiplication
  + # (e.g., can return after enqueing the operation on a CUDA stream).
  + # The x.numpy() call below will ensure that all enqueued operations
  + # have completed (and will also copy the result to host memory,
  + # so we're including a little more than just the matmul operation
  + # time).
  + _ = x.numpy()
  end = time.time()
  return end - start
  @@ -751,8 +757,8 @@ Output (exact numbers depend on hardware):
  ```
  Time to multiply a (1000, 1000) matrix by itself 200 times:
  -CPU: 4.614904403686523 secs
  -GPU: 0.5581181049346924 secs
  +CPU: 1.46628093719 secs
  +GPU: 0.0593810081482 secs
  ```
  A `tf.Tensor` object can be copied to a different device to execute its
  