[ENH] ✨ Support MPS accelerated OpenCLIP embeddings (#3295)

## Description of changes

Currently, attempting to use OpenCLIP embeddings on a metal performance
shader (MPS) device results in the following error:

```
RuntimeError: slow_conv2d_forward_mps: input(device='cpu') and weight(device=mps:0')  must be on the same device in add.
```

These changes fix this error by explicitly moving input tensors to the
model device in `OpenCLIPEmbeddingFunction` embedding methods. This
provides a significant speedup (~2x on my M1 MacBook Pro) compared to
running on CPU.

## Test plan

It's quite difficult to perform CI testing on real macOS machines, so
these changes don't include any tests for MPS devices specifically.
However, these changes have been tested successfully on an M1 device,
both with `device="cpu"` (the default) and `device="mps"`.

- [x] Tests pass locally with `pytest` for python, `yarn test` for js,
`cargo test` for rust

## Documentation Changes

No user-facing APIs are updated. Now `device="mps"` should work
correctly as expected by users.

chromadb/utils/embedding_functions/open_clip_embedding_function.py

@@ -47,6 +47,7 @@ def __init__(
         model, _, preprocess = open_clip.create_model_and_transforms(
             model_name=model_name, pretrained=checkpoint
         )
+        self._device = device
         self._model = model
         self._model.to(device)
         self._preprocess = preprocess
@@ -56,14 +57,16 @@ def _encode_image(self, image: Image) -> Embedding:
         pil_image = self._PILImage.fromarray(image)
         with self._torch.no_grad():
             image_features = self._model.encode_image(
-                self._preprocess(pil_image).unsqueeze(0)
+                self._preprocess(pil_image).unsqueeze(0).to(self._device)
             )
             image_features /= image_features.norm(dim=-1, keepdim=True)
             return cast(Embedding, image_features.squeeze().cpu().numpy())
 
     def _encode_text(self, text: Document) -> Embedding:
         with self._torch.no_grad():
-            text_features = self._model.encode_text(self._tokenizer(text))
+            text_features = self._model.encode_text(
+                self._tokenizer(text).to(self._device)
+            )
             text_features /= text_features.norm(dim=-1, keepdim=True)
             return cast(Embedding, text_features.squeeze().cpu().numpy())