Background:
In Huggingface Transformers' image processor, e.g. CLIPImageProcessor, the constructor requires input of input_data_format, which gives the Image's color channels being in the first or the last position in its shape.

For example, if an image's shape is (512, 512, 3), it means its resolution is 512*512 pixels, and it has RBG, 3 color channels. In this case, input_data_format is ImageChannelDimension.LAST or ChannelDimension.LAST in Transformers.

Sometimes, people would use customized Image format in a shape of (3, 512, 512) for performance purpose. Transformers requires users to point it out, or it would infer to tell it from its shape.

Generally, an image would have 1 or 3 color channels representing Gray or RGB. So, the inferring algorithm in Transformers looks for 1 or 3 values in the image's shape.

If your input images are in the shape of (3, xxx, 1) or (1, xxx, 3), the inferring algorithm would get confused, and raise following exception:
'The channel dimension is ambiguous. Got image shape (1, xxx, 3). Assuming channels are the first dimension.' 'ValueError: mean must have 1 elements if it is an iterable, got 3'

Fix:
1. Add a class ImageChannelDimension to define 2 possible Image color channels position in an Image's shape
2. Input this information in model.encode method, and pass it to Tokenizer and image processor from Transformers.

1. Add doc-string for newly added 'image_channel_dimension' parameter of 'encode' function.
2. Changed the parameter's name from 'input_data_format' to 'image_channel_dimension'.

1. To make the 'tokenize' interface compatible between Texts and Images.

And fixed Conflicts in:
sentence_transformers/SentenceTransformer.py