code: def get_pretrained_file_names(embedding_name=None):
      """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)`.
      """
  
      text_embedding_reg = registry.get_registry(_TokenEmbedding)
  
      if embedding_name is not None:
          if embedding_name not in text_embedding_reg:
              raise KeyError(
                  f"Cannot find `embedding_name` {embedding_name}. Use "
                  "`get_pretrained_file_names("
                  "embedding_name=None).keys()` to get all the valid embedding "
                  "names."
              )
          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()
          }
  
imports: import logging
  import os
  import tarfile
  import warnings
  import zipfile
  from .None import _constants
  from .None import vocab
  from ...None import ndarray
  from ...None import registry
  from ...None import base
  from ...util import is_np_array
  from ...None import numpy
  from ...None import numpy_extension
label_desc: Use image node_id 3 for calling a global function with example usage: get_pretrained_file_names(embedding_name) and returns: list, unknown