def METHOD_NAME(model: BertModel, ckpt_dir: str, model_name: str):
    """
    Args:
        model: BertModel Pytorch model instance to be converted
        ckpt_dir: Tensorflow model directory
        model_name: model name
    Currently supported HF models:
        - Y BertModel
        - N BertForMaskedLM
        - N BertForPreTraining
        - N BertForMultipleChoice
        - N BertForNextSentencePrediction
        - N BertForSequenceClassification
        - N BertForQuestionAnswering
    """
    tensors_to_transpose = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
    var_map = (
        ("layer.", "layer_"),
        ("word_embeddings.weight", "word_embeddings"),
        ("position_embeddings.weight", "position_embeddings"),
        ("token_type_embeddings.weight", "token_type_embeddings"),
        (".", "/"),
        ("LayerNorm/weight", "LayerNorm/gamma"),
        ("LayerNorm/bias", "LayerNorm/beta"),
        ("weight", "kernel"),
    )
    if not os.path.isdir(ckpt_dir):
        os.makedirs(ckpt_dir)
    state_dict = model.state_dict()
    def to_tf_var_name(name: str):
        for patt, repl in iter(var_map):
            name = name.replace(patt, repl)
        return f"bert/{name}"
    def create_tf_var(tensor: np.ndarray, name: str, session: tf.Session):
        tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
        tf_var = tf.get_variable(dtype=tf_dtype, shape=tensor.shape, name=name, initializer=tf.zeros_initializer())
        session.run(tf.variables_initializer([tf_var]))
        session.run(tf_var)
        return tf_var
    tf.reset_default_graph()
    with tf.Session() as session:
        for var_name in state_dict:
            tf_name = to_tf_var_name(var_name)
            torch_tensor = state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose):
                torch_tensor = torch_tensor.T
            tf_var = create_tf_var(tensor=torch_tensor, name=tf_name, session=session)
            tf.keras.backend.set_value(tf_var, torch_tensor)
            tf_weight = session.run(tf_var)
            print(f"Successfully created {tf_name}: {np.allclose(tf_weight, torch_tensor)}")
        saver = tf.train.Saver(tf.trainable_variables())
        saver.save(session, os.path.join(ckpt_dir, model_name.replace("-", "_") + ".ckpt"))