Replace einsum with unsqueeze
Author: xenova
Pull request: yes

# 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

Comments:
**[For maintainers]** Suggested jobs to run (before merge)

run-slow: vjepa2

Related commit: https://huggingface.co/onnx-community/vjepa2-vitl-fpc32-256-diving48-ONNX/commit/79226a125eb1323ffff2e555acf77d1a0de15548

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_39234). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

run-slow: vjepa2

This comment contains run-slow, running the specified jobs: 

models: ['models/vjepa2']
quantizations: [] ...