(batch_size, sequence_length) which is why we see the same axes used in the
configuration.
Notice that inputs property for DistilBertOnnxConfig returns an
OrderedDict. This ensures that the inputs are matched with their relative
position within the PreTrainedModel.forward() method when tracing the graph.
We recommend using an OrderedDict for the inputs and outputs properties
when implementing custom ONNX configurations.
Once you have implemented an ONNX configuration, you can instantiate it by
providing the base model's configuration as follows:
```python
from transformers import AutoConfig
config = AutoConfig.from_pretrained("distilbert-base-uncased")
onnx_config = DistilBertOnnxConfig(config)
```
The resulting object has several useful properties. For example you can view the
ONNX operator set that will be used during the export:
