Repository: huggingface/transformers
Pull request: Replace einsum with unsqueeze

# What does this PR do?

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Fixes an issue when exporting VJEPA-2 to ONNX:
> InferenceError: [ShapeInferenceError] (op_type:Einsum, node name: /vjepa2/encoder/layer.0/attention/Einsum): Inputs has inconsistent type tensor(float)

It also makes things more readable for users who don't undertand einsum notation 😅 

Code to reproduce:
```py
import torch
from transformers import  AutoModelForVideoClassification
hf_repo = "facebook/vjepa2-vitl-fpc32-256-diving48"
model = AutoModelForVideoClassification.from_pretrained(hf_repo)

pixel_values_videos = torch.randn(2, 16, 3, 224, 224)
torch.onnx.export(model,                     # model being run
                  (pixel_values_videos, ),   # model input (or a tuple for multiple inputs)
                  "model.onnx",              # where to save the model (can be a file or file-like object)
                  export_params=True,        # store the trained parameter weights inside the model file
                  opset_version=18,          # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['pixel_values_videos'],   # the model's input names
                  output_names = ['logits'], # the model's output names
                  dynamic_axes={'pixel_values_videos' : {0: 'batch_size', 1: 'num_frames', 3: 'height', 4: 'width'},    # variable length axes
                                'logits' : {0: 'batch_size'}},
                )

import onnx
onnx.checker.check_model("model.onnx", full_check=True)
```

### Before
```
InferenceError: [ShapeInferenceError] (op_type:Einsum, node name: /vjepa2/encoder/layer.0/attention/Einsum): Inputs has inconsistent type tensor(float)
```

### After
```
no error
```

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@qubvel

Changed files:
filename: src/transformers/models/vjepa2/modeling_vjepa2.py
patch: @@ -187,7 +187,7 @@ def rotate_queries_or_keys(x, pos):
     omega = torch.arange(D // 2, dtype=x.dtype, device=x.device)
     omega /= D / 2.0
     omega = 1.0 / 10000**omega  # (D/2,)
-    freq = torch.einsum("..., f -> ... f", pos, omega)  # (..., N, D/2), outer product
+    freq = pos.unsqueeze(-1) * omega  # (..., N, D/2), outer product
 
     # -- build rotation matrix and apply
     emb_sin = freq.sin()  # (..., N, D/2)