code: def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs):
          
          embedding = TokenEmbedding(**kwargs)
          embedding._load_embedding(file_path, elem_delim=elem_delim, encoding=encoding)
          return embedding
docs: Creates a user-defined token embedding from a pre-trained embedding file.
  
  
          This is to load embedding vectors from a user-defined pre-trained token embedding file.
          For example, if `elem_delim` = ' ', the expected format of a custom pre-trained token
          embedding file may look like:
  
          'hello 0.1 0.2 0.3 0.4 0.5\\\\nworld 1.1 1.2 1.3 1.4 1.5\\\\n'
  
          where embedding vectors of words `hello` and `world` are [0.1, 0.2, 0.3, 0.4, 0.5] and
          [1.1, 1.2, 1.3, 1.4, 1.5] respectively.
  
  
          Parameters
          ----------
          file_path : str
              The path to the user-defined pre-trained token embedding file.
          elem_delim : str, default ' '
              The delimiter for splitting a token and every embedding vector element value on the same
              line of the custom pre-trained token embedding file.
          encoding : str, default 'utf8'
              The encoding scheme for reading the custom pre-trained token embedding file.
          kwargs : dict
              All other keyword arguments are passed to the TokenEmbedding initializer.
  
  
          Returns
          -------
          instance of :class:`gluonnlp.embedding.TokenEmbedding`
              The user-defined token embedding instance.