def __init__(self, transformer_type: str='Bert', transformer_model_key: str='bert-base-uncased', transformer_tokenizer_key: str=None, hidden: Union[(List[int], int)]=(- 2), reduce_option: str='mean', sentence_handler: SentenceHandler=SentenceHandler(), random_state: int=12345, hidden_concat: bool=False, gpu_id: int=0):
    '\n        :param transformer_type: The Transformer type, such as Bert, GPT2, DistilBert, etc.\n        :param transformer_model_key: The transformer model key. This is the directory for the model.\n        :param transformer_tokenizer_key: The transformer tokenizer key. This is the tokenizer directory.\n        :param hidden: The hidden output layers to use for the summarization.\n        :param reduce_option: The reduce option, such as mean, max, min, median, etc.\n        :param sentence_handler: The sentence handler class to process the raw text.\n        :param random_state: The random state to use.\n        :param hidden_concat: Deprecated hidden concat option.\n        :param gpu_id: GPU device index if CUDA is available. \n        '
    try:
        self.MODEL_DICT['Roberta'] = (RobertaModel, RobertaTokenizer)
        self.MODEL_DICT['Albert'] = (AlbertModel, AlbertTokenizer)
        self.MODEL_DICT['Camembert'] = (CamembertModel, CamembertTokenizer)
        self.MODEL_DICT['Bart'] = (BartModel, BartTokenizer)
        self.MODEL_DICT['Longformer'] = (LongformerModel, LongformerTokenizer)
        self.MODEL_DICT['BigBird'] = (BigBirdModel, BigBirdTokenizer)
    except Exception:
        pass
    (model_clz, tokenizer_clz) = self.MODEL_DICT[transformer_type]
    model = model_clz.from_pretrained(transformer_model_key, output_hidden_states=True)
    tokenizer = tokenizer_clz.from_pretrained((transformer_tokenizer_key if (transformer_tokenizer_key is not None) else transformer_model_key))
    super().__init__(None, model, tokenizer, hidden, reduce_option, sentence_handler, random_state, hidden_concat, gpu_id)
:param transformer_type: The Transformer type, such as Bert, GPT2, DistilBert, etc.
:param transformer_model_key: The transformer model key. This is the directory for the model.
:param transformer_tokenizer_key: The transformer tokenizer key. This is the tokenizer directory.
:param hidden: The hidden output layers to use for the summarization.
:param reduce_option: The reduce option, such as mean, max, min, median, etc.
:param sentence_handler: The sentence handler class to process the raw text.
:param random_state: The random state to use.
:param hidden_concat: Deprecated hidden concat option.
:param gpu_id: GPU device index if CUDA is available.
def __init__(self, transformer_type: str='Bert', transformer_model_key: str='bert-base-uncased', transformer_tokenizer_key: str=None, hidden: Union[(List[int], int)]=(- 2), reduce_option: str='mean', sentence_handler: SentenceHandler=SentenceHandler(), random_state: int=12345, hidden_concat: bool=False, gpu_id: int=0):
    '\n        :param transformer_type: The Transformer type, such as Bert, GPT2, DistilBert, etc.\n        :param transformer_model_key: The transformer model key. This is the directory for the model.\n        :param transformer_tokenizer_key: The transformer tokenizer key. This is the tokenizer directory.\n        :param hidden: The hidden output layers to use for the summarization.\n        :param reduce_option: The reduce option, such as mean, max, min, median, etc.\n        :param sentence_handler: The sentence handler class to process the raw text.\n        :param random_state: The random state to use.\n        :param hidden_concat: Deprecated hidden concat option.\n        :param gpu_id: GPU device index if CUDA is available. \n        '
    try:
        self.MODEL_DICT['Roberta'] = (RobertaModel, RobertaTokenizer)
        self.MODEL_DICT['Albert'] = (AlbertModel, AlbertTokenizer)
        self.MODEL_DICT['Camembert'] = (CamembertModel, CamembertTokenizer)
        self.MODEL_DICT['Bart'] = (BartModel, BartTokenizer)
        self.MODEL_DICT['Longformer'] = (LongformerModel, LongformerTokenizer)
        self.MODEL_DICT['BigBird'] = (BigBirdModel, BigBirdTokenizer)
    except Exception:
        pass
    (model_clz, tokenizer_clz) = self.MODEL_DICT[transformer_type]
    model = model_clz.from_pretrained(transformer_model_key, output_hidden_states=True)
    tokenizer = tokenizer_clz.from_pretrained((transformer_tokenizer_key if (transformer_tokenizer_key is not None) else transformer_model_key))
    super().__init__(None, model, tokenizer, hidden, reduce_option, sentence_handler, random_state, hidden_concat, gpu_id)