def METHOD_NAME(
    self,
    images: Union[np.ndarray, torch.Tensor, Sequence[Union[np.ndarray, torch.Tensor]]],
    name: str = 'Images',
    channels_last: bool = False,
    step: Optional[int] = None,
    masks: Optional[Dict[str, Union[np.ndarray, torch.Tensor, Sequence[Union[np.ndarray, torch.Tensor]]]]] = None,
    mask_class_labels: Optional[Dict[int, str]] = None,
    use_table: bool = True,
):
    """Log images. Logs any tensors or arrays as images.
    Args:
        images (np.ndarray | torch.Tensor | Sequence[np.ndarray | torch.Tensor]): Dictionary mapping
            image(s)' names (str) to an image of array of images.
        name (str): The name of the image(s). (Default: ``'Images'``)
        channels_last (bool): Whether the channel dimension is first or last.
            (Default: ``False``)
        step (Optional[int], optional): The current step or batch of training at the
            time of logging. Defaults to None. If not specified the specific
            LoggerDestination implementation will choose a step (usually a running
            counter).
        masks (Dict[str, np.ndarray | torch.Tensor | Sequence[np.ndarray | torch.Tensor]], optional): A dictionary
            mapping the mask name (e.g. predictions or ground truth) to a sequence of masks.
        mask_class_labels (Dict[int, str], optional): Dictionary mapping label id to its name. Used for labelling
            each color in the mask.
        use_table (bool): Whether to make a table of the images or not. (default: ``True``). Only for use
            with WandB.
    """
    del images, name, channels_last, step, masks, mask_class_labels, use_table
    pass