Support `xser` for saving checkpoints for XLA

Nobody has claimed this yet.

## 🚀 Feature

```python
import torch_xla.utils.serialization as xser

xser.save(model.state_dict(), path)
```

### Motivation

In case where memory is limited compared to the size of the model parameters, an API is provided that reduces the memory footprint on the host:

```python
import torch_xla.utils.serialization as xser

xser.save(model.state_dict(), path)
```

This API streams XLA tensors to CPU one at a time, reducing the amount of host memory used, but it requires a matching load API to restore:

```python
import torch_xla.utils.serialization as xser

state_dict = xser.load(path)
model.load_state_dict(state_dict)
```

### Pitch

### Alternatives

### Additional context

cc @borda @kaushikb11 @rohitgr7

Start by reading the torch_xla.utils.serialization API shown in the issue and trace the existing checkpoint serialization path. The work is done when matching save and load APIs support model state dictionaries for XLA while streaming tensors to CPU one at a time to reduce host memory use.

Written by the indexing model from the issue text.