vision.ImageSegmenter fails when image array is F-continguous

When using numpy in python, the ordering of array elements in memory is handled under the hood and usually the user never has to worry about this. For instance, when I read an image file from disk parse it as a numpy array with image = np.asarray(Image.open('image.jpg')), it is loaded in C-contiguous format. If I decide to crop the image by slicing (crop = image[20:-20, 30:-3]) I will get an F-contiguous array as a result.

If I try to run segmentation inference using the vision.ImageSegmenter.segment() method with a C-contiguous array as input, it works fine. If the input image array is F-contiguous, the model happily returns garbage.

Versions:
numpy: 1.23.1
python 3.9.10
tflife-support: 0.4.2
tensorflow: 2.8.3

Suggested fix:
Add the following to the inference method:

    if not image_array.data.c_contiguous:
        image_array = np.ascontiguousarray(image_array)

Start at the vision.ImageSegmenter.segment() entry point and reproduce the issue with C-contiguous and F-contiguous NumPy image arrays. Verify that segmentation results are correct for both layouts; the issue's suggested NumPy conversion describes the expected direction, but no file or test path is provided.