Improve mismatched sizes management when loading a pretrained model
# What does this PR do?

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Examples currently fail when the loaded model head has different dimensions from the expected ones. For instance, in the image classification example, if a pretrained classification head has different dimensions from the classification head to fine-tune, the current implementation will lead to this error:
```
Traceback (most recent call last):
  File "run_image_classification.py", line 377, in <module>
    main()
  File "run_image_classification.py", line 267, in main
    model = AutoModelForImageClassification.from_pretrained(
  File "/home/regis/HuggingFace/dev/transformers/venv/lib/python3.8/site-packages/transformers/models/auto/auto_factory.py", line 446, in from_pretrained
    return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
  File "/home/regis/HuggingFace/dev/transformers/venv/lib/python3.8/site-packages/transformers/modeling_utils.py", line 2067, in from_pretrained
    model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
  File "/home/regis/HuggingFace/dev/transformers/venv/lib/python3.8/site-packages/transformers/modeling_utils.py", line 2276, in _load_pretrained_model
    raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for SwinForImageClassification:
	size mismatch for classifier.weight: copying a param with shape torch.Size([1000, 1024]) from checkpoint, the shape in current model is torch.Size([3, 1024]).
	size mismatch for classifier.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([3]).
```

To reproduce this, you can run the image classification example with Swin, such as:
```
python run_image_classification.py \
  --dataset_name beans \
  --output_dir /tmp/beans_outputs/ \
  --remove_unused_columns False \
  --do_train \
  --per_device_train_batch_size 8 \
  --model_name_or_path microsoft/swin-base-patch4-window7-224
```

The solution is to add the argument `ignore_mismatched_sizes=True` to the `AutoModelForXXX.from_pretrained` method. Thus, this PR does the following:
- expand the error message and suggest a solution when the error is raised
- for all classification examples, an argument `--ignore_mismatched_sizes` can now be given to adapt the dimensions of the classification head when they are different from the expected ones


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 -->

_The documentation is not available anymore as the PR was closed or merged._<|||||>I see that all tests corresponding to mismatched sizes failed. Looking at [test_modeling_common.py](https://github.com/huggingface/transformers/blob/ee393c009a243bbb86fa11d2efa771a1704d85cf/tests/test_modeling_common.py#L2191), I guess the current behaviour is expected.

Shall I close this PR and open a new issue regarding the fact that some models will fail in examples (such as Swin for image classification as explained in the first message)?<|||||>I'm not sure why you would open a new issue: this is not a bug and there is an option to load a model with a pretrained head that has different shapes from the checkpoint. Or do you mean the examples should have a flag to activate that option? <|||||>> I'm not sure why you would open a new issue: this is not a bug and there is an option to load a model with a pretrained head that has different shapes from the checkpoint. Or do you mean the examples should have a flag to activate that option?

After using it a bit more I realized it's not a bug indeed, sorry for the confused wording. The problem is that just changing the model used in the example may break it and I couldn't fine anywhere in the doc or in the READMEs how to solve this, I had to take a look at the code. I suggest one of the following to make it more user-friendly:
- the error message suggests to add the argument `ignore_mismatched_sizes=True` to `AutoModelForXXX.from_pretrained`
- adding a flag to activate that option as you propose, with a mention in the README
- changing the default value of `ignore_mismatched_sizes` to `True` since a warning is displayed when sizes are different, but I guess I'm lacking of context here and I'm just considering this example use case

Again I'm certainly lacking of context here but I would be happy to modify this PR so that it makes using a different model in examples less tedious when the pretrained head has different dimensions :)<|||||>>     * the error message suggests to add the argument `ignore_mismatched_sizes=True` to `AutoModelForXXX.from_pretrained`

This is definitely something we can add and would help the user!

>     * adding a flag to activate that option as you propose, with a mention in the README

Yes, another welcome improvement!

>     * changing the default value of `ignore_mismatched_sizes` to `True` since a warning is displayed when sizes are different, but I guess I'm lacking of context here and I'm just considering this example use case

This can't be done for backward compatibility reasons. In further work in `from_pretrained`, we might have a default that does this, but only for the head of the model. It's dangerous to have it enabled by default on the whole body.<|||||>Great, I'm going to modify this PR accordingly!<|||||>I just modified the *classification* examples because I'm not sure about the types of head used in other scenarios.

Also, I noticed that VSCode automatically trimmed extra whitespaces (because I configured it this way). Let me know if this is an issue and I'll revert that.