def call(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        encoder_outputs: Optional[TFBaseModelOutput] = None,
        past_key_values=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        labels=None,
        training=False,
    ):
        """
        Returns:

        Examples:

        ```python
        >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
        >>> import tensorflow as tf
        >>> mname = '{{cookiecutter.checkpoint_identifier}}'
        >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
        >>> TXT = "My friends are <mask> but they eat too many carbs."
        >>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
        >>> batch = tokenizer([TXT], return_tensors='tf')
        >>> logits = model(inputs=batch.input_ids).logits
        >>> probs = tf.nn.softmax(logits[0])
        >>> # probs[5] is associated with the mask token
        ```"""

        if labels is not None:
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training
        )
        lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
        lm_logits = self.bias_layer(lm_logits)
        masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return TFSeq2SeqLMOutput(
            loss=masked_lm_loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,  # index 1 of d outputs
            decoder_hidden_states=outputs.decoder_hidden_states,  # index 2 of d outputs
            decoder_attentions=outputs.decoder_attentions,  # index 3 of d outputs
            cross_attentions=outputs.cross_attentions,  # index 4 of d outputs
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,  # index 0 of encoder outputs
            encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
            encoder_attentions=outputs.encoder_attentions,  # 2 of e out
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
        >>> import tensorflow as tf
        >>> mname = '{{cookiecutter.checkpoint_identifier}}'
        >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
        >>> TXT = "My friends are <mask> but they eat too many carbs."
        >>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
        >>> batch = tokenizer([TXT], return_tensors='tf')
        >>> logits = model(inputs=batch.input_ids).logits
        >>> probs = tf.nn.softmax(logits[0])
        >>> # probs[5] is associated with the mask token
        ```
dpkd/transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py