Add EfficientNet Image PreProcessor
Author: zshn25
Pull request: yes

# What does this PR do?

Add Fast Image Processor https://github.com/huggingface/transformers/issues/36978 for EfficientNet

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Fixes # (issue)


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3 tests fail but can't figure out why.

```bash
======================================================================= short test summary info =======================================================================
FAILED tests/models/efficientnet/test_image_processing_efficientnet.py::EfficientNetImageProcessorTest::test_rescale - AssertionError: False is not true
FAILED tests/models/efficientnet/test_image_processing_efficientnet.py::EfficientNetImageProcessorTest::test_slow_fast_equivalence - AssertionError: False is not true
FAILED tests/models/efficientnet/test_image_processing_efficientnet.py::EfficientNetImageProcessorTest::test_slow_fast_equivalence_batched - AssertionError: False is not true
==================================================================== 3 failed, 17 passed in 3.93s =====================================================================
```

`test_rescale` passes when inputs are casted to `np.float64` rather than the default `np.float32`. Made necessary change to the test method.

Thanks to https://github.com/huggingface/transformers/pull/37094#issuecomment-2763288730, changing the test resamping to bilinear passes the equivalence tests

Hi @chewyuenrachael and @Yann-CV thank you for working on your PRs https://github.com/huggingface/transformers/pull/37119 https://github.com/huggingface/transformers/pull/37094 . I reviewed this one as it was the first to be posted, but it looks like your PR also helped here so thanks a lot!

All tests pass except one, which is unrelated to this PR

```
FAILED tests/models/idefics2/test_modeling_idefics2.py::Idefics2ForConditionalGenerationModelTest::test_constrained_beam_search_generate_dict_output - RuntimeError: shape mismatch: value tensor of shape [32, 64] cannot be broadcast to indexing result of shape [34, 64]```

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_37055). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hello!

This change introduces a dependency of `torchvision>=0.19.0`, whereas previously I was able to use older versions. Even worse: when using older versions of `torchvision`, importing parts of `transformers` will quietly (!!!) fail, e.g.:
```
ImportError: cannot import name 'PreTrainedModel' from 'transformers'
```
when running
```python
from transformers import PreTrainedModel
```

The underlying source only becomes apparent when importing with the full path:
```python
from transformers.modeling_utils import PreTrainedModel
```
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "[sic]\lib\site-packages\transformers\modeling_utils.py", line 74, in <module>
    from .loss.loss_utils import LOSS_MAPPING
  File "[sic]\lib\site-packages\transformers\loss\loss_utils.py", line 21, in <module>
    from .loss_d_fine import DFineForObjectDetectionLoss
  File "[sic]\lib\site-packages\transformers\loss\loss_d_fine.py", line 21, in <module>
    from .loss_for_object_detection import (
  File "[sic]\lib\site-packages\transformers\loss\loss_for_object_detection.py", line 32, in <module>
    from transformers.image_transforms import center_to_corners_format
  File "[sic]\lib\site-packages\transformers\image_transforms.py", line 22, in <module>
    from .image_utils import (
  File "[sic]\lib\site-packages\transformers\image_utils.py", line 62, in <module>
    PILImageResampling.NEAREST: InterpolationMode.NEAREST_EXACT,
  File "C:\Users\tom\.conda\envs\setfit\lib\enum.py", line 429, in __getattr__
    raise AttributeError(name) from None
AttributeError: NEAREST_EXACT
```

I'm fine with upgrading my `torchvision`, but perhaps that should be required (via `setup.py`), and we really should be wary with hidden import errors.

- Tom Aarsen