# tf.numpy_function

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/script_ops.py#L684-L768) |

Wraps a python function and uses it as a TensorFlow op.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.numpy_function`](https://www.tensorflow.org/api_docs/python/tf/numpy_function)

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    tf.numpy_function(
        func, inp, Tout, stateful=True, name=None
    )

Given a python function `func` wrap this function as an operation in a
TensorFlow function. `func` must take numpy arrays as its arguments and
return numpy arrays as its outputs.

The following example creates a TensorFlow graph with `np.sinh()` as an
operation in the graph:

    def my_numpy_func(x):
      # x will be a numpy array with the contents of the input to the
      # tf.function
      return np.sinh(x)
    @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
    def tf_function(input):
      y = tf.numpy_function(my_numpy_func, [input], tf.float32)
      return y * y
    tf_function(tf.constant(1.))
    <tf.Tensor: shape=(), dtype=float32, numpy=1.3810978>

Comparison to [`tf.py_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/py_function):
[`tf.py_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/py_function) and [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) are very similar, except that
[`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) takes numpy arrays, and not [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor)s. If you want the
function to contain `tf.Tensors`, and have any TensorFlow operations executed
in the function be differentiable, please use [`tf.py_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/py_function).

> [!NOTE]
> **Note:** We recommend to avoid using [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) outside of prototyping and experimentation due to the following known limitations:

- Calling [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) will acquire the Python Global Interpreter Lock
  (GIL) that allows only one thread to run at any point in time. This will
  preclude efficient parallelization and distribution of the execution of the
  program. Therefore, you are discouraged to use [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) outside
  of prototyping and experimentation.

- The body of the function (i.e. `func`) will not be serialized in a
  `tf.SavedModel`. 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.numpy_function()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function). If you are using distributed
  TensorFlow, you must run a [`tf.distribute.Server`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/distribute/Server) in the same process as the
  program that calls [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) you must pin the created
  operation to a device in that server (e.g. using `with tf.device():`).

- Currently [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) is not compatible with XLA. Calling
  [`tf.numpy_function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/numpy_function) inside [`tf.function(jit_compile=True)`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/function) will raise an
  error.

- Since the function takes numpy arrays, you cannot take gradients
  through a numpy_function. If you require something that is differentiable,
  please consider using tf.py_function.

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| ## Args ||
|---|---|
| `func` | A Python function, which accepts `numpy.ndarray` objects as arguments and returns a list of `numpy.ndarray` objects (or a single `numpy.ndarray`). This function must accept as many arguments as there are tensors in `inp`, and these argument types will match the corresponding [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) objects in `inp`. The returns `numpy.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 [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/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.) Setting this argument to False tells the runtime to treat the function as stateless, which enables certain optimizations. A function is stateless when given the same input it will return the same output and have no side effects; its only purpose is to have a return value. The behavior for a stateful function with the `stateful` argument False is undefined. In particular, caution should be taken when mutating the input arguments as this is a stateful operation. |
| `name` | (Optional) A name for the operation. |

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| ## Returns ||
|---|---|
| Single or list of [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) which `func` computes. ||

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