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:
            decorated_forward = self.convert_input_output(
                input_names_to_handle=input_names_to_handle, output_type=output_type
            )(super().__call__)
            _output_image = decorated_forward(*inputs, **kwargs)
            if output_type == "tensor":
                self._output_image = self._detach_tensor_to_cpu(_output_image)
            else:
                self._output_image = _output_image
        else:
            _output_image = super().__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/core/module.py