def __call__(self,
                 inputs: InputType,
                 batch_size: int = 1,
                 **kwargs) -> dict:
        """Call the inferencer.

        Args:
            inputs (str | array | list): The image path or array, or a list of
                images.
            batch_size (int): Batch size. Defaults to 1.
            **kwargs: Other keyword arguments accepted by the `extract_feat`
                method of the model.

        Returns:
            tensor | Tuple[tensor]: The extracted features.
        """
        ori_inputs = self._inputs_to_list(inputs)
        inputs = self.preprocess(ori_inputs, batch_size=batch_size)
        preds = []
        for data in inputs:
            preds.extend(self.forward(data, **kwargs))

        return preds
Call the inferencer.

        Args:
            inputs (str | array | list): The image path or array, or a list of
                images.
            batch_size (int): Batch size. Defaults to 1.
            **kwargs: Other keyword arguments accepted by the `extract_feat`
                method of the model.

        Returns:
            tensor | Tuple[tensor]: The extracted features.
        
mmpretrain/apis/feature_extractor.py