Porting of 2d_autoencoderkl and 3d_autoencoderkl tutorials from MONAI generative (#1823)

Fixes # .

### Description
Incorporation of tutorials for AuoencoderKL (2D and 3D) as part of the
porting of MONAI Generative Models.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [X] Avoid including large-size files in the PR.
- [X] Clean up long text outputs from code cells in the notebook.
- [X ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [X] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

---------

Signed-off-by: Virginia Fernandez <virginia.fernandez@kcl.ac.uk>
Signed-off-by: Eric Kerfoot <eric.kerfoot@kcl.ac.uk>
Co-authored-by: Virginia Fernandez <virginia.fernandez@kcl.ac.uk>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <eric.kerfoot@kcl.ac.uk>

Author: 5 people

generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb

@@ -63,3 +63,12 @@ Example shows how to use a DDPM to inpaint of 2D images from the MedNIST dataset
 
 ## [Guiding the 2D diffusion synthesis using ControlNet](./controlnet/2d_controlnet.ipynb)
 Example shows how to use ControlNet to condition a diffusion model trained on 2D brain MRI images on binary brain masks.
+
+## [Spatial variational autoencoder for 2D modelling and synthesis](./2d_autoencoderkl)
+Example shows the use cases of applying a spatial VAE to a 2D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones.
+
+## [Spatial variational autoencoder for 3D modelling and synthesis](./3d_autoencoderkl)
+Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones.
+
+## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb)
+Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance).