def capture_model_internals(self) -> Iterator[dict]:
        """
        Context manager that captures the internal-module outputs of
        this predictor's model. The idea is that you could use it as follows:

        .. code-block:: python

            with predictor.capture_model_internals() as internals:
                outputs = predictor.predict_json(inputs)

            return {**outputs, "model_internals": internals}
        """
        results = {}
        hooks = []

        # First we'll register hooks to add the outputs of each module to the results dict.
        def add_output(idx: int):
            def _add_output(mod, _, outputs):
                results[idx] = {"name": str(mod), "output": sanitize(outputs)}
            return _add_output

        for idx, module in enumerate(self._model.modules()):
            if module != self._model:
                hook = module.register_forward_hook(add_output(idx))
                hooks.append(hook)

        # If you capture the return value of the context manager, you get the results dict.
        yield results

        # And then when you exit the context we remove all the hooks.
        for hook in hooks:
            hook.remove()
Context manager that captures the internal-module outputs of
        this predictor's model. The idea is that you could use it as follows:

        .. code-block:: python

            with predictor.capture_model_internals() as internals:
                outputs = predictor.predict_json(inputs)

            return {**outputs, "model_internals": internals}