Function py_func_common

tensorflow/python/ops/script_ops.py:428–495

Wraps a python function and uses it as a TensorFlow op. Given a python function `func`, which takes numpy arrays as its arguments and returns numpy arrays as its outputs, wrap this function as an operation in a TensorFlow graph. The following snippet constructs a simple TensorFlow graph tha

(func, inp, Tout, stateful=True, name=None)

def py_func_common(func, inp, Tout, stateful=True, name=None):
  """Wraps a python function and uses it as a TensorFlow op.

  Given a python function `func`, which takes numpy arrays as its
  arguments and returns numpy arrays as its outputs, wrap this function as an
  operation in a TensorFlow graph. The following snippet constructs a simple
  TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation
  in the graph:

  ```python
  def my_func(x):
    # x will be a numpy array with the contents of the placeholder below
    return np.sinh(x)
  input = tf.compat.v1.placeholder(tf.float32)
  y = tf.compat.v1.py_func(my_func, [input], tf.float32)
  ```

  **N.B.** The `tf.compat.v1.py_func()` operation has the following known
  limitations:

  * The body of the function (i.e. `func`) will not be serialized in a
    `GraphDef`. Therefore, you should not use this function if you need to
    serialize your model and restore it in a different environment.

  * The operation must run in the same address space as the Python program
    that calls `tf.compat.v1.py_func()`. If you are using distributed
    TensorFlow, you
    must run a `tf.distribute.Server` in the same process as the program that
    calls
    `tf.compat.v1.py_func()` and you must pin the created operation to a device
    in that
    server (e.g. using `with tf.device():`).

  Args:
    func: A Python function, which accepts `ndarray` objects as arguments and
      returns a list of `ndarray` objects (or a single `ndarray`). This function
      must accept as many arguments as there are tensors in `inp`, and these
      argument types will match the corresponding `tf.Tensor` objects in `inp`.
      The returns `ndarray`s must match the number and types defined `Tout`.
      Important Note: Input and output numpy `ndarray`s of `func` are not
        guaranteed to be copies. In some cases their underlying memory will be
        shared with the corresponding TensorFlow tensors. In-place modification
        or storing `func` input or return values in python datastructures
        without explicit (np.)copy can have non-deterministic consequences.
    inp: A list of `Tensor` objects.
    Tout: A list or tuple of tensorflow data types or a single tensorflow data
      type if there is only one, indicating what `func` returns.
    stateful: (Boolean.) If True, the function should be considered stateful. If
      a function is stateless, when given the same input it will return the same
      output and have no observable side effects. Optimizations such as common
      subexpression elimination are only performed on stateless operations.
    name: A name for the operation (optional).

  Returns:
    A list of `Tensor` or a single `Tensor` which `func` computes.
  """
  if context.executing_eagerly():
    result = func(*[x.numpy() for x in inp])