code: def load_embeddings(embd_dir: str) -> dict:
      embd_dic = OrderedDict(np.load(embd_dir))
      embd_dic2 = {}
      for k, v in embd_dic.items():
          if len(v.shape) == 1:
              v = v[None, :]
          embd_dic2[k] = torch.nn.functional.normalize(
              torch.from_numpy(v), p=2, dim=1
          ).squeeze()
  
      return embd_dic2
  
file_path: espnet/egs2/misp2021/asr1/pyscripts/utils/spk_apply_score_norm.py
imports: import sys
  from collections import OrderedDict
  import numpy
  import torch
  import yaml
  from espnet2.torch_utils.device_funcs import to_device
label_desc: Use image node_id 1 for calling a global function with example usage: load_embeddings(embd_dir) and returns: embd_dic2