# Visualizer Function

Tensorleap enables visualizations of data within the model graph by connecting a [**Visualizer**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/visualizer-node.md) block to a node's output tensor. This allows you to visualize the model's input, prediction, ground truth, or any internal tensor.

By default, the following naive Visualizers are provided (in the UI):

* Image
* Graph
* Numeric
* HorizontalBar
* Text
* ImageMask
* TextMask

These naive visualizers shows the data as-is, but in many cases, there is a need to write our own **Visualizer Function** to make sense of the data. For example:

* Converting *tokenized* data to text *words*
* Custom post-processing
* Draw landmarks on images
* Apply transforms

Example of a visualizer function:

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

@tensorleap_custom_visualizer(name="input_visualizer", visualizer_type=LeapDataType.Text)
def input_visualizer(input_ids: np.ndarray) -> LeapText:
    input_ids = np.squeeze(input_ids)
    text = decode_token_ids(input_ids)
    return LeapText(text)
```

The [@tensorleap\_custom\_visualizer](/tensorleap-integration/python-api/code_loader/decorators/tensorleap_custom_visualizer.md)decorator registers each visualizer into the Tensorleap integration.

These functions should return one of the following types defined in [**visualizer\_classes**](/tensorleap-integration/python-api/code_loader/visualizer_classes.md).

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


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