code: def _append_unk_vecs(matrix, vocab_size):
      append_dim = vocab_size - len(matrix)
      assert append_dim in [0, 1], "Error occurs in the embedding file."
      if append_dim == 1:
          # there is no unknown_token in the embedding file
          mean = np.mean(found_vectors, axis=0, keepdims=True)
          std = np.std(found_vectors, axis=0, keepdims=True)
          vecs = (
              np.random.randn(append_dim, dim).astype("float32") * std
              + mean
          )
          return np.concatenate([matrix, vecs], axis=0)
      return matrix
  
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 2 for calling a global function with example usage: _append_unk_vecs(matrix, vocab_size) and returns: matrix, np