message: Updated doc for py_function
diff: @@ -313,7 +313,7 @@ def eager_py_func(func, inp, Tout, name=None):
  This function allows expressing computations in a TensorFlow graph as
  Python functions. In particular, it wraps a Python function `func`
  in a once-differentiable TensorFlow operation that executes it with eager
  - execution enabled. As a consequence, `tf.contrib.eager.py_func` makes it
  + execution enabled. As a consequence, `tf.py_function` makes it
  possible to express control flow using Python constructs (`if`, `while`,
  `for`, etc.), instead of TensorFlow control flow constructs (`tf.cond`,
  `tf.while_loop`). For example, you might use `tf.py_function` to
  @@ -339,11 +339,11 @@ def eager_py_func(func, inp, Tout, name=None):
  y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0})
  ```
  - You can also use `tf.contrib.eager.py_func` to debug your models at runtime
  + You can also use `tf.py_function` to debug your models at runtime
  using Python tools, i.e., you can isolate portions of your code that
  you want to debug, wrap them in Python functions and insert `pdb` tracepoints
  or print statements as desired, and wrap those functions in
  - `tf.contrib.eager.py_func`.
  + `tf.py_function`.
  For more information on eager execution, see the
  [Eager guide](https://tensorflow.org/guide/eager).
  @@ -352,7 +352,7 @@ def eager_py_func(func, inp, Tout, name=None):
  the latter, the former lets you use TensorFlow operations in the wrapped
  Python function. In particular, while `tf.py_func` only runs on CPUs and
  wraps functions that take NumPy arrays as inputs and return NumPy arrays as
  - outputs, `tf.contrib.eager.py_func` can be placed on GPUs and wraps functions
  + outputs, `tf.py_function` can be placed on GPUs and wraps functions
  that take Tensors as inputs, execute TensorFlow operations in their bodies,
  and return Tensors as outputs.
  @@ -366,7 +366,7 @@ def eager_py_func(func, inp, Tout, name=None):
  * The operation must run in the same address space as the Python program
  that calls `tf.py_function()`. If you are using distributed
  TensorFlow, you must run a `tf.train.Server` in the same process as the
  - program that calls `tf.contrib.eager.py_func()` and you must pin the created
  + program that calls `tf.py_function()` and you must pin the created
  operation to a device in that server (e.g. using `with tf.device():`).
  