def call(self, inputs, **kwargs):
        r"""

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (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` (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 DistilBertTokenizer, TFDistilBertForMaskedLM

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        prediction_scores = outputs[0]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_states = distilbert_output[0]  # (bs, seq_length, dim)
        prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
        prediction_logits = self.act(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_projector(prediction_logits)

        outputs = (prediction_logits,) + distilbert_output[1:]
        return outputs  # logits, (hidden_states), (attentions)


    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (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` (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 DistilBertTokenizer, TFDistilBertForMaskedLM

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        prediction_scores = outputs[0]

        
xtune/src/transformers/modeling_tf_distilbert.py