Add support for PyTorch

The first thing to do is:

• Add PyTorch to pyproject.toml as an optional dependency, and make it a new extra option.
• Add PyTorch to the test option, so that it will be installed before running pytest. This would be the ideal setup, but I'm not sure if it can be installed in the same environment of TensorFlow.
• Regenerate the lock file, by manually running the CI job. This must be done on the docker image used for the CI, and not on your pc.
• Download the artifact with the lock file, and upload it to the repo.

Now you can add a new PyTorchModel class, inside the surrogates module. For that to work, you'll have to provide a concrete implementation for:

• _load_model()
• _save_model()
• _predict_model_jacobian()
• _predict_model_jvp()
• _predict_model_output()
• _predict_model_output_and_jacobian_fwd()
• _predict_model_output_and_jacobian_rev()
• _predict_model_vjp()

After this, you should:

• Add the test to reach 100% coverage. These should cover the SISO, SIMO, MISO and MIMO cases. I prefer to keep the repo light, and waste some CI time, therefore the models should be generated during the CI, and never uploaded to the repo.
• The OpenMDAO components should work out of the box, but adding some test for that would be nice.
• Write the documentation.
• Increment the version number. Since I'm trying to use semantic versioning, you should change the MINOR version.

Please check the existing code for TensorFlow for a guideline.