def call(
        self,
        pixel_values: TFModelInputType | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFData2VecVisionModelOutputWithPooling]:
        r"""
        bool_masked_pos (`tf.Tensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        outputs = self.data2vec_vision(
            pixel_values=pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs

        bool_masked_pos (`tf.Tensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        
dpkd/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py