Batched predictor inference returns inconsistently shaped masks

When performing batch inference using `SAM2ImagePredictor`, if you pass a singleton batch of points/labels, you get masks of shape (3, H,W):
```python
>>> predictor.set_image(image)
>>> point_coords.shape # Singleton batch
(1, 2, 2)
>>> masks, _ , _ = predictor.predict(point_coords=point_coords, point_label=point_labels)
>>> masks.shape
(3, 1200,1800)
```
However, when your batch size is N>1 your masks become shape (N, 3, H, W). This makes it difficult to use the predictor interface where the batch size is arbitrary (and user-specified).