# set\_unlabeled\_data\_preprocess

The `leap_binder.set_unlabeled_data_preprocess` binding function points to a preprocessing, similar to the [**Preprocessing Function**](/tensorleap-integration/writing-integration-code/preprocess-function.md), but points to unlabeled data lacking the ground-truth values.

This is often used to prioritize samples for labeling, which can be done by using the [**Fetch Similar**](/user-interface/dashboards/dashlets/sample-analysis.md#fetch-similar) analysis.

```python
code_loader.leap_binder.set_unlabeled_data_preprocess(
    function=Callable[[], PreprocessResponse]
)
```

<table><thead><tr><th width="158.46928201888204">Args</th><th></th></tr></thead><tbody><tr><td><code>function</code></td><td><em>(Callable)</em> This parameter points to the <a href="/pages/5EZX1Hijc7xSpyYFPaVG"><strong>Preprocess Function</strong></a>  mentioned above.</td></tr></tbody></table>

### Examples

#### Basic Usage

```python
from code_loader import leap_binder
from code_loader.contract.datasetclasses import PreprocessingResponse

# Preprocessing Function
def unlabeled_preprocessing_func() -> PreprocessingResponse:
...
    return PreprocessingResponse(length=len(unlabeled_df), data=unlabeled_df)

leap_binder.set_unlabeled_data_preprocess(function=unlabeled_preprocessing_func)
```

Usage within the full script can be found at the [**Dataset Script**](/tensorleap-integration/writing-integration-code.md#dataset-script).

#### Guides

Full examples can be found at the **Dataset Integration** section of the following guides:

* [**MNIST Guide**](/guides/full-guides/mnist-guide.md)
* [**IMDB Guide**](/guides/full-guides/imdb-guide.md)


---

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