# Preprocess Function

The `preprocessing_func` *(custom name)* is a **preprocess** function that is called just once before the training/evaluating process. It prepares the data for later use in **input encoders**, **output encoders**, and **metadata** functions.

```python
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_preprocess

@tensorleap_preprocess()
def preprocessing_func() -> List[PreprocessResponse]:
    ...
    train = PreprocessResponse(sample_ids=list(train_df.index), data=train_df, state=DataStateType.training)
    val = PreprocessResponse(sample_ids=list(val_df.index), data=val_df, state=DataStateType.validation)
    test = PreprocessResponse(sample_ids=list(test_df.index), data=test_df, state=DataStateType.test)
    unlabeled = PreprocessResponse(sample_ids=list(unlabeled_df.index), data=unlabeled_df, state=DataStateType.unlabeled)
​
    return [train, val, test, unlabeled]
```

The [@tensorleap\_preprocess](/tensorleap-integration/python-api/code_loader/decorators/tensorleap_preprocess.md) decorator registers the preprocess function into the Tensorleap integration.

This function returns a `List` of [**`PreprocessResponse`**](/tensorleap-integration/python-api/code_loader/datasetclasses/preprocessresponse.md) objects. The elements on that list correspond to the `train` , `validation,` `test` and `unlabeled` data slices.

For a successful Tensorleap integration, supplying a train and validation set is mandatory, the rest is optional. Refer to the [**Code Integration**](/tensorleap-integration/writing-integration-code.md) section for complete usage instructions.

{% hint style="warning" %}
It is mandatory to return at least a training and a validation set.
{% endhint %}


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