code: def _save_feature(self, file_name, feature, info):
      file_base_name = os.path.basename(file_name)
      file_base_name = file_base_name.split(".")[0]
      info_file_base_name = file_base_name + "_info.npy"
      file_base_name = file_base_name + ".npy"
  
      np.save(
          os.path.join(self.args.output_folder, file_base_name),
          feature.cpu().numpy(),
      )
      np.save(
          os.path.join(self.args.output_folder, info_file_base_name),
          info,
      )
  
imports: import argparse
  import os
  import cv2
  import numpy
  import torch
  from maskrcnn_benchmark.config import cfg
  from maskrcnn_benchmark.layers import nms
  from maskrcnn_benchmark.modeling.detector import build_detection_model
  from maskrcnn_benchmark.structures.image_list import to_image_list
  from maskrcnn_benchmark.utils.model_serialization import load_state_dict
  from mmf.utils.download import download
  from PIL import Image
  from tools.scripts.features.extraction_utils import chunks, get_image_files
label_desc: Use image node_id 8 for calling the FeatureExtractor obj's underlying member method code with example usage: obj._save_feature(file_name, feature, info) without return types