def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
        loss (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
        start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
            Span-start scores (before SoftMax).
        end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
            Span-end scores (before SoftMax).
        mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    Examples::

        import tensorflow as tf
        from transformers import XLNetTokenizer, TFXLNetForQuestionAnsweringSimple

        tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
        model = TFXLNetForQuestionAnsweringSimple.from_pretrained('xlnet-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
        outputs = model(input_ids)
        start_scores, end_scores = outputs[:2]

        """
        transformer_outputs = self.transformer(inputs, **kwargs)

        sequence_output = transformer_outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)

        outputs = (start_logits, end_logits,) + transformer_outputs[
            1:
        ]  # Keep mems, hidden states, attentions if there are in it

        return outputs  # start_logits, end_logits, (mems), (hidden_states), (attentions)

    Returns:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
        loss (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
        start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
            Span-start scores (before SoftMax).
        end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
            Span-end scores (before SoftMax).
        mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` or :obj:`Numpy array` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    Examples::

        import tensorflow as tf
        from transformers import XLNetTokenizer, TFXLNetForQuestionAnsweringSimple

        tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
        model = TFXLNetForQuestionAnsweringSimple.from_pretrained('xlnet-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
        outputs = model(input_ids)
        start_scores, end_scores = outputs[:2]

        
xtune/src/transformers/modeling_tf_xlnet.py