Redundant to_channel_dimension_format() call makes preprocessing fail in case the image has height of 1 pixel
Author: dhansmair
Pull request: yes

In the `resize()` function in image_transforms.py, the line 267: I think `image = to_channel_dimension_format(image, ChannelDimension.LAST)` is redundant as this conversion is also applied in the following `to_pil_image()`.

This redundant call actually makes the clip preprocessing fail in special cases. The problem can be reproduced with the following code snippet:
```python
import torch
from transformers.models.clip import CLIPFeatureExtractor
vision_processor = CLIPFeatureExtractor.from_pretrained('openai/clip-vit-large-patch14')
images = [
    torch.rand(size=(3, 2, 10), dtype=torch.float),
    torch.rand(size=(3, 10, 1), dtype=torch.float),
    torch.rand(size=(3, 1, 10), dtype=torch.float)
]
for image in images:
    processed_image = vision_processor(images=image, return_tensors="pt")['pixel_values']
    print(processed_image.shape)
    assert processed_image.shape == torch.Size([1, 3, 224, 224])
```

The last image has a height of 1 pixel.
The second call to `to_channel_dimesion_format()` will transpose the image, and the height dimension is wrongly treated as the channels dimension afterwards. Because of this, the following normalize() step will result in an exception.

An image of height 1 pixel honestly doesn't make much sense, but it happened in my training on visual genome region descriptions and took me a while to track down the problem.

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Comments:
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cc @amyeroberts 

sure thing!