# @tensorleap\_input\_encoder

The `tensorleap_input_encoder` decorates an [**Input Encoder**](/tensorleap-integration/writing-integration-code/input-encoder.md)

```python
@tensorleap_input_encoder(name='image', channel_dim=-1)
def input_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray:
    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>input</strong>, e.g. image.</td></tr><tr><td><code>channel_dim</code></td><td>(int, optional) The dimension of the channels in the result. Default is -1 (channel last).<br><br>Example:<br>If return shape of the function is [H,W,3] -> channel_dim=-1<br>If return shape of the function is [3,H,W] -> channel_dim=1</td></tr></tbody></table>

Returns:

a np.ndarray typed input (without a batch dimension)

### Examples

#### Basic Usage

```python
import numpy as np
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_input_encoder
...
@tensorleap_input_encoder('image')
def input_encoder(idx: int, preprocess: PreprocessResponse) -> np.ndarray:
    return preprocess.data['images'][idx].astype('float32')
```

Usage within the full script can be found at [**Dataset Script**](/tensorleap-integration/writing-integration-code.md#dataset-script).

#### Guides

Full examples can be found at 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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