# set\_visualizer

The `leap_binder.set_visualizer` binding function points to [**Visualizer Function**](/tensorleap-integration/writing-integration-code/visualizer-function.md). In addition, it defines the `visualizer_type` and `name` for reference.

```python
code_loader.leap_binder.set_visualizer(
    name=str,
    function=VisualizerCallableInterface,
    visualizer_type=LeapDataType,
    heatmap_visualizer=Optional[Callable[[npt.NDArray[np.float32]], npt.NDArray[np.float32]]]
)
```

<table><thead><tr><th width="158.46928201888204">Args</th><th></th></tr></thead><tbody><tr><td><code>function</code></td><td><em>(</em>VisualizerCallableInterface<em>)</em> This parameter points to the <a href="/pages/ftQvXx4Uv1fL8darU8tg"><strong>Visualizer Function</strong></a> mentioned above.</td></tr><tr><td><code>name</code></td><td><em>(str)</em> with the given name of the <strong>input,</strong> e.g. image.</td></tr><tr><td><code>visualizer_type</code></td><td><em>(</em><a href="/pages/XVWuIgRFJgiODi1OYyUs"><em><strong>LeapDataType</strong></em></a><em>)</em> This property sets the type of the data to be visualized by the visualizer.</td></tr><tr><td><code>heatmap_visualizer</code></td><td><p><em>(optional)</em></p><p><em>Callable[[npt.NDArray[np.float32]], npt.NDArray[np.float32]]</em><br>This parameter points to a <strong>function</strong> that modifies the heatmap data before visualization.</p></td></tr></tbody></table>

### Examples

#### Basic Usage

```python
import numpy as np
from code_loader.contract.visualizer_classes import LeapText
from code_loader import leap_binder
...

def text_visualizer_func(data: np.ndarray) -> LeapText:
    tokenizer = leap_binder.custom_tokenizer
    text = tokenizer.sequences_to_texts(data)
    return LeapText(text)
    
leap_binder.set_visualizer(
    function=text_visualizer_func,
    visualizer_type=LeapText.type,
    name='text_from_token'
)
```

The [**decoder\_classes**](/tensorleap-integration/python-api/code_loader/visualizer_classes.md) pages contain additional, decoder specific, examples. Moreover, full script usage can be found in [**Integration Script**](/tensorleap-integration/writing-integration-code.md#dataset-script).

#### Resize Image and Heatmap

When changing the original data shape in the *visualizer function,* we need to reshape the heatmap data that is projected. `heatmap_visualizer` points to a function that modifies the heatmap data respectively. Consider the following example:

```python
from code_loader.contract.visualizer_classes import LeapImage
from code_loader import le

def resized_image_visualizer(data: npt.NDArray[np.float32]) -> LeapImage:
    return LeapImage(np.resize(data, (256, 512, 3)))

def resized_image_visualizer_heatmap(data: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]:
    # data is the heatmap with original size (origin_W, origin_H)
    return np.resize(data, (256, 512))    # we reshape to the resized shape


leap_binder.set_visualizer(
    function=resized_image_visualizer,
    name='resized_image_visualizer',
    visualizer_type=LeapImage.type,
    heatmap_visualizer=resized_image_visualizer_heatmap
)
```

#### Guides

Full examples can be found in 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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