code: def filter_embeddings(embeddings, vocab, dim):
      """Loads word vectors in numpy array.
  
      Args:
          embeddings (dict): a dictionary of numpy array.
          vocab (dict): word_index lookup table.
  
      Returns:
          numpy array: an array of word embeddings.
      """
      if not isinstance(embeddings, dict):
          return
      _embeddings = np.zeros([len(vocab), dim])
      for word in vocab:
          if word in embeddings:
              word_idx = vocab[word]
              _embeddings[word_idx] = embeddings[word]
  
      return _embeddings
  
imports: from ....imports import *
label_desc: Use image node_id 3 for calling a global function with example usage: filter_embeddings(embeddings, vocab, dim) and returns: _embeddings