code: def read(self, image_feat_path):
      image_info = self._load(image_feat_path)
      if self.first:
          self.first = False
          if (
              image_info["features"].size == 1
              and "image_feat" in image_info["features"].item()
          ):
              self.take_item = True
  
      image_feature = image_info["features"]
  
      if self.take_item:
          item = image_info["features"].item()
          if "image_text" in item:
              image_info["image_text"] = item["image_text"]
              image_info["is_ocr"] = item["image_bbox_source"]
              image_feature = item["image_feat"]
  
          if "info" in item:
              if "image_text" in item["info"]:
                  image_info.update(item["info"])
              image_feature = item["feature"]
  
      # Handle case of features with class probs
      if (
          image_info["features"].size == 1
          and "features" in image_info["features"].item()
      ):
          item = image_info["features"].item()
          image_feature = item["features"]
          image_info["image_height"] = item["image_height"]
          image_info["image_width"] = item["image_width"]
  
          # Resize these to self.max_loc
          image_loc, _ = image_feature.shape
          image_info["cls_prob"] = np.zeros(
              (self.max_loc, item["cls_prob"].shape[1]),
              dtype=np.float32,
          )
          image_info["cls_prob"][0:image_loc] = item["cls_prob"][
              : self.max_loc, :
          ]
          image_info["bbox"] = np.zeros(
              (self.max_loc, item["bbox"].shape[1]), dtype=np.float32
          )
          image_info["bbox"][0:image_loc] = item["bbox"][
              : self.max_loc, :
          ]
          image_info["num_boxes"] = item["num_boxes"]
  
      # Handle the case of ResNet152 features
      if len(image_feature.shape) > 2:
          shape = image_feature.shape
          image_feature = image_feature.reshape(-1, shape[-1])
  
      image_loc, image_dim = image_feature.shape
      tmp_image_feat = np.zeros(
          (self.max_loc, image_dim), dtype=np.float32
      )
      tmp_image_feat[0:image_loc] = image_feature[
          : self.max_loc, :
      ]  # noqa
      image_feature = torch.from_numpy(tmp_image_feat)
  
      del image_info["features"]
      image_info["max_features"] = torch.tensor(
          image_loc, dtype=torch.long
      )
      return image_feature, 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 3 for calling the PaddedFasterRCNNFeatureReader obj's underlying member method code with example usage: obj.read(image_feat_path) and returns: image_feature,  image_info