# @tensorleap\_custom\_visualizer

The `tensorleap_custom_visualizer` decorates a [**Visualizer Function**](/tensorleap-integration/writing-integration-code/visualizer-function.md).

```python
@tensorleap_custom_visualizer(name='image_visualizer',
                              visualizer_type=LeapDataType.Image,
                              heatmap_function=None)
def image_visualizer(image: np.ndarray) -> LeapImage:
    pass
```

<table><thead><tr><th width="158.46928201888204">Args</th><th></th></tr></thead><tbody><tr><td><code>name</code></td><td><em>(str)</em> with the given name of the <strong>visualization,</strong> e.g. image_visualzier.</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>heatmap_function</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>

#### Visualizer inputs:

np.ndarray tensors with a batch dimension. This batch would always be 1.

#### Visualizers outputs:

One of the [visualizer objects](/tensorleap-integration/python-api/code_loader/visualizer_classes.md) contraining a squeezed (no-batch) object.

### Examples

#### Basic Usage

```python
import numpy as np
from code_loader.contract.visualizer_classes import LeapImage
from code_loader.contract.enums import LeapDataType

...

@tensorleap_custom_visualizer(name='image_visualizer', visualizer_type=LeapDataType.Image)
def image_visualizer(image: np.ndarray) -> LeapImage:
    image = image / image.max()
    image = image.squeeze(0)
    return LeapImage(image)
```

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
import numpy.typing as npt

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
    
@tensorleap_custom_visualizer(name='image_visualizer',
                              visualizer_type=LeapDataType.Image,
                              heatmap_function=resized_image_visualizer_heatmap)
def resized_image_visualizer(data: npt.NDArray[np.float32]) -> LeapImage:
    return LeapImage(np.resize(data, (256, 512, 3)))

```

#### 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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