code: def get_pretrained_file_names(embedding_name=None):
      
  
      text_embedding_reg = registry.get_registry(_TokenEmbedding)
  
      if embedding_name is not None:
          if embedding_name not in text_embedding_reg:
              raise KeyError('Cannot find `embedding_name` %s. Use '
                             '`get_pretrained_file_names('
                             'embedding_name=None).keys()` to get all the valid embedding '
                             'names.' % embedding_name)
          return list(text_embedding_reg[embedding_name].pretrained_file_name_sha1.keys())
      else:
          return {embedding_name: list(embedding_cls.pretrained_file_name_sha1.keys())
                  for embedding_name, embedding_cls in registry.get_registry(_TokenEmbedding).items()}
docs: Get valid token embedding names and their pre-trained file names.
  
  
      To load token embedding vectors from an externally hosted pre-trained token embedding file,
      such as those of GloVe and FastText, one should use
      `mxnet.contrib.text.embedding.create(embedding_name, pretrained_file_name)`.
      This method returns all the valid names of `pretrained_file_name` for the specified
      `embedding_name`. If `embedding_name` is set to None, this method returns all the valid
      names of `embedding_name` with their associated `pretrained_file_name`.
  
  
      Parameters
      ----------
      embedding_name : str or None, default None
          The pre-trained token embedding name.
  
  
      Returns
      -------
      dict or list:
          A list of all the valid pre-trained token embedding file names (`pretrained_file_name`)
          for the specified token embedding name (`embedding_name`). If the text embeding name is
          set to None, returns a dict mapping each valid token embedding name to a list of valid
          pre-trained files (`pretrained_file_name`). They can be plugged into
          `mxnet.contrib.text.embedding.create(embedding_name,
          pretrained_file_name)`.