def __call__(
        self,
        *inputs: Any,
        input_names_to_handle: Optional[List[Any]] = None,
        output_type: str = "tensor",
        **kwargs: Any,
    ) -> Any:
        """Overwrite the __call__ function to handle various inputs.

        Args:
            inputs: Inputs to operate on.
            input_names_to_handle: List of input names to convert, if None, handle all inputs.
            output_type: Desired output type ('tensor', 'numpy', or 'pil').
            kwargs: Additional arguments.

        Returns:
            Callable: Decorated function with converted input and output types.

        """
        # Wrap the forward method with the decorator
        if not self._disable_features:
            # TODO: Some more behaviour for AugmentationSequential needs to be revisited later
            # e.g. We convert only images, etc.
            decorated_forward = self.convert_input_output(
                input_names_to_handle=input_names_to_handle, output_type=output_type
            )(super(ImageSequential, self).__call__)
            _output_image = decorated_forward(*inputs, **kwargs)

            if len(inputs) == 1 and isinstance(inputs[0], dict):
                original_keys, in_data_keys, inputs, invalid_data = self._preproc_dict_data(inputs[0])
            else:
                in_data_keys = kwargs.get("data_keys", self.data_keys)
            data_keys = self.transform_op.preproc_datakeys(in_data_keys)

            if len(data_keys) > 1 and DataKey.INPUT in data_keys:
                # NOTE: we may update it later for more supports of drawing boxes, etc.
                idx = data_keys.index(DataKey.INPUT)
                if output_type == "tensor":
                    self._output_image = _output_image
                    if isinstance(_output_image, dict):
                        self._output_image[original_keys[idx]] = _output_image[original_keys[idx]]
                    else:
                        self._output_image[idx] = _output_image[idx]
                elif isinstance(_output_image, dict):
                    self._output_image[original_keys[idx]] = _output_image[original_keys[idx]]
                else:
                    self._output_image[idx] = _output_image[idx]
            else:
                self._output_image = _output_image
        else:
            _output_image = super(ImageSequential, self).__call__(*inputs, **kwargs)
        return _output_image
Overwrite the __call__ function to handle various inputs.

        Args:
            inputs: Inputs to operate on.
            input_names_to_handle: List of input names to convert, if None, handle all inputs.
            output_type: Desired output type ('tensor', 'numpy', or 'pil').
            kwargs: Additional arguments.

        Returns:
            Callable: Decorated function with converted input and output types.

        
kornia/augmentation/container/augment.py