use:
```python
from transformers import AutoConfig
config = AutoConfig.from_pretrained("distilbert-base-uncased")
onnx_config_for_seq_clf = DistilBertOnnxConfig(config, task="sequence-classification")
print(onnx_config_for_seq_clf.outputs)
OrderedDict([('logits', {0: 'batch'})])
```
All of the base properties and methods associated with [~onnx.config.OnnxConfig] and the
other configuration classes can be overriden if needed. Check out
[BartOnnxConfig] for an advanced example.
Exporting the model
Once you have implemented the ONNX configuration, the next step is to export the
model. Here we can use the export() function provided by the
transformers.onnx package. This function expects the ONNX configuration, along
with the base model and tokenizer, and the path to save the exported file:
```python
