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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs):
 """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. """

        embedding = TokenEmbedding(**kwargs)
        embedding._load_embedding(file_path, elem_delim=elem_delim, encoding=encoding)
        return embedding