Construct an embedding block.

def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
        """ Construct an embedding block. """
        if embed is None:
            assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
                                           'token_type_embed is not given.'
            with self.name_scope():
                embed = nn.HybridSequential(prefix=prefix)
                with embed.name_scope():
                    embed.add(nn.Embedding(input_dim=vocab_size, output_dim=embed_size,
                                           weight_initializer=initializer))
                    if dropout:
                        embed.add(nn.Dropout(rate=dropout))
        assert isinstance(embed, Block)
        return embed