code: def _load_embedding_npz(file_path, vocab, unknown):
      if vocab is not None:
          result = np.zeros(len(vocab), dtype=bool)
      else:
          result = []
      npz_dict = np.load(file_path, allow_pickle=True)
      unknown_token = npz_dict["unknown_token"]
      if not unknown_token:
          unknown_token = unknown
      else:
          if isinstance(unknown_token, np.ndarray):
              if unknown_token.dtype.kind == "S":
                  unknown_token = unknown_token.tobytes().decode()
              else:
                  unknown_token = str(unknown_token)
      if unknown != unknown_token:
          warnings.warn(
              "You may not assign correct unknown token in the pretrained file"
              "Use {} as the unknown mark.".format(unknown_token)
          )
  
      idx_to_token = npz_dict["idx_to_token"].tolist()
      token2idx = {x: i for i, x in enumerate(idx_to_token)}
      idx_to_vec = npz_dict["idx_to_vec"]
      if vocab is None:
          result = Vocab(idx_to_token, unk_token=unknown_token)
          idx_to_vec = _append_unk_vecs(idx_to_vec, len(result))
          return idx_to_vec, result
      else:
          matrix = np.random.randn(
              len(vocab), idx_to_vec.shape[-1]
          ).astype("float32")
          for i, token in enumerate(vocab.all_tokens):
              if token == vocab.unk_token and unknown_token is not None:
                  word = unknown_token
              else:
                  word = token
              if word in token2idx:
                  index = token2idx[word]
                  matrix[i] = idx_to_vec[index]
                  result[i] = True
          return matrix, result
  
file_path: gluon-nlp/src/gluonnlp/embedding/embed_loader.py
imports: import io
  import logging
  import os
  import warnings
  import fasttext
  import numpy
  from mxnet.gluon.utils import download, check_sha1, _get_repo_file_url
  from .None import _constants
  from ..base import get_home_dir
  from ..data import Vocab
label_desc: Use image node_id 4 for calling a global function with example usage: _load_embedding_npz(file_path, vocab, unknown) and returns: idx_to_vec,  result, matrix,  result