message: Add an example to tf.config.experimental_run_functions_eagerly doc
diff: @@ -256,14 +256,40 @@ RUN_FUNCTIONS_EAGERLY = False
  def run_functions_eagerly(run_eagerly):
  """Enables / disables eager execution of `tf.function`s.
  - After calling `tf.config.experimental_run_functions_eagerly(True)` all
  - invocations of tf.function will run eagerly instead of running through a graph
  + Calling `tf.config.experimental_run_functions_eagerly(True)` will make all
  + invocations of `tf.function` run eagerly instead of running as a traced graph
  function.
  - This can be useful for debugging or profiling.
  -
  - Similarly, calling `tf.config.experimental_run_functions_eagerly(False)` will
  - revert the behavior of all functions to graph functions.
  + This can be useful for debugging or profiling. For example, let's say you
  + implemented a simple iterative sqrt function, and you want to collect the
  + intermediate values and plot the convergence. Appending the values to a list
  + in `@tf.function` normally wouldn't work since it will just record the Tensors
  + being traced, not the values. Instead, you can do the following.
  +
  + >>> ys = []
  + >>>
  + >>> @tf.function
  + ... def sqrt(x):
  + ... y = x / 2
  + ... d = y
  + ... for _ in range(10):
  + ... d /= 2
  + ... if y * y < x:
  + ... y += d
  + ... else:
  + ... y -= d
  + ... ys.append(y.numpy())
  + ... return y
  + >>>
  + >>> tf.config.experimental_run_functions_eagerly(True)
  + >>> sqrt(tf.constant(2.))
  + <tf.Tensor: id=..., shape=(), dtype=float32, numpy=1.4150391>
  + >>> ys
  + [1.5, 1.25, 1.375, 1.4375, 1.40625, 1.421875, 1.4140625, 1.4179688, 1.4160156,
  + 1.4150391]
  +
  + Calling `tf.config.experimental_run_functions_eagerly(False)` will undo this
  + behavior.
  Args:
  run_eagerly: Boolean. Whether to run functions eagerly.
  