    def __call__(self, clip):
        """
        Args:
        clip (list): list of PIL.Image
        Returns:
        list PIL.Image : list of transformed PIL.Image
        """
        if isinstance(clip[0], np.ndarray):
            raise TypeError(
                'Color jitter not yet implemented for numpy arrays')
        elif isinstance(clip[0], PIL.Image.Image):
            brightness, contrast, saturation, hue = self.get_params(
                self.brightness, self.contrast, self.saturation, self.hue)

            # Create img transform function sequence
            img_transforms = []
            if brightness is not None:
                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness))
            if saturation is not None:
                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation))
            if hue is not None:
                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue))
            if contrast is not None:
                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast))
            random.shuffle(img_transforms)

            # Apply to all images
            jittered_clip = []
            for img in clip:
                for func in img_transforms:
                    jittered_img = func(img)
                jittered_clip.append(jittered_img)

        else:
            raise TypeError('Expected numpy.ndarray or PIL.Image' +
                            'but got list of {0}'.format(type(clip[0])))
        return jittered_clip
Args:
clip (list): list of PIL.Image
Returns:
list PIL.Image : list of transformed PIL.Image
videomamba/video_sm/datasets/video_transforms.py