input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
resulting in more calculations.
It is recommended to be careful of the total number of operations done on each input and to follow performance closely
when exporting varying sequence-length models.
Using TorchScript in Python
Below is an example, showing how to save, load models as well as how to use the trace for inference.
Saving a model
This snippet shows how to use TorchScript to export a BertModel. Here the BertModel is instantiated according
to a BertConfig class and then saved to disk under the filename traced_bert.pt
```python
from transformers import BertModel, BertTokenizer, BertConfig
