def METHOD_NAME(self, dataset, split, indices=None, width=1024, height=768):
    """Visualize a dataset.
    Example:
        Minimal example for visualizing a dataset::
            import open3d.ml.torch as ml3d  # or open3d.ml.tf as ml3d
            dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')
            vis = ml3d.vis.Visualizer()
            vis.visualize_dataset(dataset, 'all', indices=range(100))
    Args:
        dataset: The dataset to use for visualization.
        split: The dataset split to be used, such as 'training'
        indices: An iterable with a subset of the data points to visualize, such as [0,2,3,4].
        width: The width of the visualization window.
        height: The height of the visualization window.
    """
    # Setup the labels
    lut = LabelLUT()
    color_map = dataset.color_map
    for id_color, val in dataset.label_to_names.items():
        lut.add_label(str(id_color), id_color, color=color_map[id_color])
    self.set_lut("labels", lut)
    self._consolidate_bounding_boxes = True
    self._init_dataset(dataset, split, indices)
    self._visualize("Open3D - " + dataset.name, width, height)