# tf.numpy_function

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|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 2 version](https://www.tensorflow.org/api_docs/python/tf/numpy_function) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/script_ops.py#L519-L521) |

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), \`tf.compat.v2.numpy_function\`

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

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:

    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.numpy_function(my_func, [input], tf.float32)

> [!NOTE]
> **Note:** The [`tf.compat.v1.numpy_function()`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/numpy_function) 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.numpy_function()`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/numpy_function). If you are using distributed
  TensorFlow, you
  must run a [`tf.distribute.Server`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/distribute/Server) in the same process as the program that
  calls
  [`tf.compat.v1.numpy_function()`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/numpy_function) and you must pin the created operation to a device
  in that
  server (e.g. using `with tf.device():`).

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| ## 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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/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). |

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| ## Returns ||
|---|---|
| A list of `Tensor` or a single `Tensor` which `func` computes. ||

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