Merge pull request #577 from dianna-ai/fix-pytorch-link-in-readme

Fix link

README.md

@@ -173,7 +173,7 @@ DIANNA comes with simple datasets. Their main goal is to provide intuitive insig
 <!-- TODO: Add all links, see issue https://github.com/dianna-ai/dianna/issues/135 -->
 
 **We work with ONNX!** ONNX is a great unified neural network standard which can be used to boost reproducible science. Using ONNX for your model also gives you a boost in performance! In case your models are still in another popular DNN (deep neural network) format, here are some simple recipes to convert them:
-* [pytorch and pytorch-lightning](https://github.com/dianna-ai/dianna/blob/main/tutorials/conversion_onnx/pytorch2onnx.ipynb) - use the built-in [`torch.onnx.export`](https://pytorch.org/docs/stable/onnx.html) function to convert pytorch models to onnx, or call the built-in [`to_onnx`](https://pytorch-lightning.readthedocs.io/en/latest/deploy/production_advanced.html) function on your [`LightningModule`](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.core.LightningModule.html#pytorch_lightning.core.LightningModule) to export pytorch-lightning models to onnx.
+* [pytorch and pytorch-lightning](https://github.com/dianna-ai/dianna/blob/main/tutorials/conversion_onnx/pytorch2onnx.ipynb) - use the built-in [`torch.onnx.export`](https://pytorch.org/docs/stable/onnx.html) function to convert pytorch models to onnx, or call the built-in [`to_onnx`](https://pytorch-lightning.readthedocs.io/en/latest/deploy/production_advanced.html) function on your [`LightningModule`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.core.LightningModule.html) to export pytorch-lightning models to onnx.
 * [tensorflow](https://github.com/dianna-ai/dianna/blob/main/tutorials/conversion_onnx/tensorflow2onnx.ipynb) - use the [`tf2onnx`](https://github.com/onnx/tensorflow-onnx) package to convert tensorflow models to onnx.
 * [keras](https://github.com/dianna-ai/dianna/blob/main/tutorials/conversion_onnx/keras2onnx.ipynb) - same as the conversion from tensorflow to onnx, the [`tf2onnx`](https://github.com/onnx/tensorflow-onnx) package also supports keras.
 * [scikit-learn](https://github.com/dianna-ai/dianna/blob/main/tutorials/conversion_onnx/skl2onnx.ipynb) - use the [`skl2onnx`](https://github.com/onnx/sklearn-onnx) package to scikit-learn models to onnx.