def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: tf.Tensor | None = None,
        token_type_ids: tf.Tensor | None = None,
        inputs_embeds: tf.Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        training: bool = False,
    ) -> TFDPRContextEncoderOutput | Tuple[tf.Tensor, ...]:
        r"""
        Return:

        Examples:

        ```python
        >>> from transformers import TFDPRContextEncoder, DPRContextEncoderTokenizer

        >>> tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
        >>> model = TFDPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", from_pt=True)
        >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="tf")["input_ids"]
        >>> embeddings = model(input_ids).pooler_output
        ```
        """
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = (
                tf.ones(input_shape, dtype=tf.dtypes.int32)
                if input_ids is None
                else (input_ids != self.config.pad_token_id)
            )
        if token_type_ids is None:
            token_type_ids = tf.zeros(input_shape, dtype=tf.dtypes.int32)

        outputs = self.ctx_encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        if not return_dict:
            return outputs[1:]

        return TFDPRContextEncoderOutput(
            pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )

        Return:

        Examples:

        ```python
        >>> from transformers import TFDPRContextEncoder, DPRContextEncoderTokenizer

        >>> tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
        >>> model = TFDPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", from_pt=True)
        >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="tf")["input_ids"]
        >>> embeddings = model(input_ids).pooler_output
        ```
        
dpkd/transformers/src/transformers/models/dpr/modeling_tf_dpr.py