# @tensorleap\_integration\_test

The tensorleap\_integration\_test decorates Tensorleap's [integration test](/tensorleap-integration/integration-test.md), and is used to instruct the platform on what code interfaces should be used when analyzing your model and to locally test the validity of a Tensorleap integration script.

```python
@tensorleap_integration_test()
def integration_test(idx: int, subset: PreprocessResponse) -> None:
    pass
```

### Examples

#### Basic Usage

```python
from code_loader.plot_functions.visualize import visualize
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_integration_test

# Import your integration functions (defined in your integration script)
from leap_integration import (
    preprocess_func, input_encoder, gt_encoder,
    image_visualizer, my_metric, my_loss, load_model
)

@tensorleap_integration_test()
def integration_test(idx, subset):
    image = input_encoder(idx, subset)
    gt = gt_encoder(idx, subset)

    model = load_model()
    y_pred = model([image])

    img_vis = image_visualizer(image)
    visualize(img_vis)

    metric_res = my_metric(gt, y_pred)
    loss_res = my_loss(gt, y_pred)
    print(metric_res, loss_res)


if __name__ == '__main__':
    train, val, *_ = preprocess_func()
    for i in range(3):
        integration_test(i, train)
        integration_test(i, val)
```


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