UNETR: Transformers for 3D Medical Image Segmentation

### Model description

I would like to add a new model:

Proposed in the paper: [UNETR: Transformers for 3D Medical Image Segmentation](https://arxiv.org/abs/2103.10504)

UNEt TRansformers (UNETR) utilize a transformer as the encoder to learn sequence representations of the input volume and effectively capture the global multi-scale information, while also following the successful "U-shaped" network design for the encoder and decoder. The transformer encoder is directly connected to a decoder via skip connections at different resolutions to compute the final semantic segmentation output.

### Open source status

- [X] The model implementation is available
- [X] The model weights are available

### Provide useful links for the implementation

Model Implementation: https://github.com/Project-MONAI/research-contributions/tree/master/UNETR

Pretrained Model: https://drive.google.com/file/d/1kR5QuRAuooYcTNLMnMj80Z9IgSs8jtLO/view?usp=sharing (Based on BTCV dataset)

Start by reviewing the linked UNETR implementation in Project-MONAI/research-contributions and the provided pretrained model link, then compare the repository's existing model integration patterns. Done means adding the UNETR model and making the provided weights usable in Hugging Face Transformers.

Written by the indexing model from the issue text.