code: def read(self, image_feat_path):
      image_feat_bbox = load_feat(image_feat_path)
      image_boxes = image_feat_bbox.item().get("image_bboxes")
      tmp_image_feat = image_feat_bbox.item().get("image_feature")
      image_loc, image_dim = tmp_image_feat.shape
      tmp_image_feat_2 = np.zeros(
          (self.max_loc, image_dim), dtype=np.float32
      )
      tmp_image_feat_2[0:image_loc,] = tmp_image_feat  # noqa
      tmp_image_feat_2 = torch.from_numpy(tmp_image_feat_2)
      tmp_image_box = np.zeros((self.max_loc, 4), dtype=np.int32)
      tmp_image_box[0:image_loc] = image_boxes
      tmp_image_box = torch.from_numpy(tmp_image_box)
      image_info = {
          "image_bbox": tmp_image_box,
          "max_features": torch.tensor(image_loc, dtype=torch.int),
      }
  
      return tmp_image_feat_2, image_info
  
imports: import math
  import os
  import pickle
  from typing import Any
  import lmdb
  import numpy
  import torch
  from mmf.utils.file_io import PathManager
label_desc: Use image node_id 2 for calling the PaddedFeatureRCNNWithBBoxesFeatureReader obj's underlying member method code with example usage: obj.read(image_feat_path) and returns: tmp_image_feat_2,  image_info