def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        """
        Dummy inputs to build the network.

        Returns:
            `Dict[str, tf.Tensor]`: The dummy inputs.
        """
        dummies = {}
        for key, spec in self.input_signature.items():
            # 2 is the most correct arbitrary size. I will not be taking questions
            dummy_shape = [dim if dim is not None else 2 for dim in spec.shape]
            if spec.shape[0] is None:
                # But let's make the batch size 1 to save memory anyway
                dummy_shape[0] = 1
            dummies[key] = tf.ones(shape=dummy_shape, dtype=spec.dtype)
            if key == "token_type_ids":
                # Some models have token_type_ids but with a vocab_size of 1
                dummies[key] = tf.zeros_like(dummies[key])
        if self.config.add_cross_attention and "encoder_hidden_states" in inspect.signature(self.call).parameters:
            if "encoder_hidden_states" not in dummies:
                if self.main_input_name == "input_ids":
                    dummies["encoder_hidden_states"] = tf.ones(
                        shape=(1, 2, self.config.hidden_size), dtype=tf.float32, name="encoder_hidden_states"
                    )
                else:
                    raise NotImplementedError(
                        "Model has cross-attention but we couldn't infer the shape for the encoder hidden states. Please manually override dummy_inputs!"
                    )
        return dummies

        Dummy inputs to build the network.

        Returns:
            `Dict[str, tf.Tensor]`: The dummy inputs.
        
dpkd/transformers/src/transformers/modeling_tf_utils.py