def _get_transform(n_px: int, image_mean: List[float] = None, image_std: List[float] = None) -> torch.Tensor:
    '''This function returns a transform to preprocess the image. The processed image will be passed into
    clip model for inference.
    Args:
        n_px: the size of the processed image
        image_mean: the mean of the image used for normalization
        image_std: the std of the image used for normalization

    Returns:
        the processed image tensor with shape (3, n_px, n_px)
    '''
    img_mean = image_mean or OPENAI_DATASET_MEAN
    img_std = image_std or OPENAI_DATASET_STD
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize(img_mean, img_std),
    ])
This function returns a transform to preprocess the image. The processed image will be passed into
    clip model for inference.
    Args:
        n_px: the size of the processed image
        image_mean: the mean of the image used for normalization
        image_std: the std of the image used for normalization

    Returns:
        the processed image tensor with shape (3, n_px, n_px)
    
src/marqo/s2_inference/clip_utils.py