def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):
    "\n        args:\n            root - path to the lasot dataset.\n            image_loader (jpeg4py_loader) -  The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n                                            is used by default.\n            vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\n                    videos with subscripts -1, -3, and -5 from each class will be used for training.\n            split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\n                    vid_ids or split option can be used at a time.\n            data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n        "
    root = (env_settings().lasot_dir if (root is None) else root)
    super().__init__('LaSOT', root, image_loader)
    self.class_list = [f for f in os.listdir(self.root)]
    self.class_to_id = {cls_name: cls_id for (cls_id, cls_name) in enumerate(self.class_list)}
    self.sequence_list = self._build_sequence_list(vid_ids, split)
    if (data_fraction is not None):
        self.sequence_list = random.sample(self.sequence_list, int((len(self.sequence_list) * data_fraction)))
    self.seq_per_class = self._build_class_list()
args:
    root - path to the lasot dataset.
    image_loader (jpeg4py_loader) -  The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
                                    is used by default.
    vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the
            videos with subscripts -1, -3, and -5 from each class will be used for training.
    split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of
            vid_ids or split option can be used at a time.
    data_fraction - Fraction of dataset to be used. The complete dataset is used by default
def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):
    "\n        args:\n            root - path to the lasot dataset.\n            image_loader (jpeg4py_loader) -  The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n                                            is used by default.\n            vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\n                    videos with subscripts -1, -3, and -5 from each class will be used for training.\n            split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\n                    vid_ids or split option can be used at a time.\n            data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n        "
    root = (env_settings().lasot_dir if (root is None) else root)
    super().__init__('LaSOT', root, image_loader)
    self.class_list = [f for f in os.listdir(self.root)]
    self.class_to_id = {cls_name: cls_id for (cls_id, cls_name) in enumerate(self.class_list)}
    self.sequence_list = self._build_sequence_list(vid_ids, split)
    if (data_fraction is not None):
        self.sequence_list = random.sample(self.sequence_list, int((len(self.sequence_list) * data_fraction)))
    self.seq_per_class = self._build_class_list()