        - Validating ONNX Model output "last_hidden_state":
                -[✓] (2, 8, 768) matches (2, 8, 768)
                -[✓] all values close (atol: 1e-05)
All good, model saved at: onnx/model.onnx
This exports an ONNX graph of the checkpoint defined by the --model argument.
In this example it is distilbert-base-uncased, but it can be any model on the
Hugging Face Hub or one that's stored locally.
The resulting model.onnx file can then be run on one of the many
accelerators that support the
ONNX standard. For example, we can load and run the model with ONNX
Runtime as follows:
```python
from transformers import AutoTokenizer
from onnxruntime import InferenceSession
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
session = InferenceSession("onnx/model.onnx")
ONNX Runtime expects NumPy arrays as input
