Add usage example for DINOv2 (#37398)

* Add usage example for DINOv2

* More explicit shape names

* More verbose text

* Moved example to Notes section

* Indentation

docs/source/en/model_doc/dinov2.md

@@ -111,7 +111,42 @@ print("Predicted class:", model.config.id2label[predicted_class_idx])
 
 ## Notes
 
-- Use [torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) to speedup inference. However, it will produce some mismatched elements. The difference between the original and traced model is 1e-4.
+- The example below shows how to split the output tensor into:
+  - one embedding for the whole image, commonly referred to as a `CLS` token,
+    useful for classification and retrieval
+  - a set of local embeddings, one for each `14x14` patch of the input image,
+    useful for dense tasks, such as semantic segmentation
+
+  ```py
+  from transformers import AutoImageProcessor, AutoModel
+  from PIL import Image
+  import requests
+  
+  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+  image = Image.open(requests.get(url, stream=True).raw)
+  print(image.height, image.width)  # [480, 640]
+  
+  processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
+  model = AutoModel.from_pretrained('facebook/dinov2-base')
+  patch_size = model.config.patch_size
+  
+  inputs = processor(images=image, return_tensors="pt")
+  print(inputs.pixel_values.shape)  # [1, 3, 224, 224]
+  batch_size, rgb, img_height, img_width = inputs.pixel_values.shape
+  num_patches_height, num_patches_width = img_height // patch_size, img_width // patch_size
+  num_patches_flat = num_patches_height * num_patches_width
+  
+  outputs = model(**inputs)
+  last_hidden_states = outputs[0]
+  print(last_hidden_states.shape)  # [1, 1 + 256, 768]
+  assert last_hidden_states.shape == (batch_size, 1 + num_patches_flat, model.config.hidden_size)
+  
+  cls_token = last_hidden_states[:, 0, :]
+  patch_features = last_hidden_states[:, 1:, :].unflatten(1, (num_patches_height, num_patches_width))
+  ```
+
+- Use [torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) to speedup inference.
+  However, it will produce some mismatched elements. The difference between the original and traced model is 1e-4.
 
   ```py
   import torch