Incorrect GPU Specification and Machine Type Mapping for A100 in Vertex API

Hello,

I'd like to express my appreciation for the xmanager tool! However, I've noticed a couple of issues regarding the specification of the A100 GPU and its associated machine types in the Vertex API, which I'd like to bring to your attention:

1. **GPU Naming Discrepancy:**
According to the Google Cloud resource documentation, the correct name for the A100 GPU with 80GB is `A100_80GB`, not `A100_80GIB`. This naming inconsistency leads to an error when requesting this resource. Reference: [Google Cloud Documentation](https://cloud.google.com/vertex-ai/docs/training/configure-compute#create_custom_job_gpus-gcloud) . Additionally, I've attached an image from the documentation.

2. **Incorrect API Call Formation:**
When the `A100_80GIB` is referenced in the Vertex API, it results in a string like `'NVIDIA_TESLA_A100_80GIB'`, whereas it should be `NVIDIA_A100_80GB`. I believe this error stems from the line: `accelerator_type = 'NVIDIA_TESLA_' + str(resource).upper()` in the [vertex.py script](https://github.com/google-deepmind/xmanager/blob/main/xmanager/cloud/vertex.py#L299C28-L299C28) .
3. **Machine Type Mismatch:**
The A100_80GB GPU should be associated with machine types such as `'a2-ultragpu-1g'`, `'a2-ultragpu-2g'`, `'a2-ultragpu-4g'`, and `'a2-ultragpu-8g'`. However, the current specification only attempts to map A100 GPUs to the following machine types:
```python
_A100_GPUS_TO_MACHINE_TYPE = {
1: 'a2-highgpu-1g',
2: 'a2-highgpu-2g',
4: 'a2-highgpu-4g',
8: 'a2-highgpu-8g',
16: 'a2-megagpu-16g',
}
```
Thank you for your attention to this matter.