def __call__(self, original_images):
        """
        Args:
            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).

        Returns:
            predictions (dict):
                the output of the model for one image only.
                See :doc:`/tutorials/models` for details about the format.
        """
        with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258
            # Apply pre-processing to image.
            height, width = original_images.shape[1:3]
            batch_inputs = []
            for original_image in original_images:
                image = self.aug.get_transform(original_image).apply_image(original_image)
                image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))

                inputs = {"image": image, "height": height, "width": width}
                batch_inputs.append(inputs)
            predictions = self.model(batch_inputs)[0]
            return predictions

        Args:
            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).

        Returns:
            predictions (dict):
                the output of the model for one image only.
                See :doc:`/tutorials/models` for details about the format.
        
video_chat_with_MOSS/models/grit_src/grit/predictor.py