code: def load_from_file(self, id_):
      return (
          np.load(str(self.features_path / f"{id_}.npz"))[
              "frame_scores"
          ]
          .squeeze(1)
          .squeeze(2)
          .squeeze(2)
      )
  
imports: import json
  from collections import OrderedDict
  from pathlib import Path
  from typing import List
  import re
  import numpy
  import torch
  import torch.utils.data
  from tqdm import tqdm
  from gluoncv.torch.utils import coot_utils
label_desc: Use image node_id 3 for calling the ActivityNetVideoFeatureLoader obj's underlying member method code with example usage: obj.load_from_file(id_) and returns: np