docs: def mlt(cls, like_text, fields=None, percent_terms_to_match=None, min_term_freq=None, max_query_terms=None, stop_words=None, min_doc_freq=None, max_doc_freq=None, min_word_len=None, max_word_len=None, boost_terms=None, boost=None, analyzer=None):
          '''
          http://www.elasticsearch.org/guide/reference/query-dsl/mlt-query.html
          More like this query find documents that are "like" provided text by running it against one or more fields.
          > query = ElasticQuery().mlt('text like this one', fields=['post.content'])
          '''
  
          instance = cls(more_like_this={'like_text': like_text})
          if fields is not None:
              instance['more_like_this']['fields'] = fields
          if percent_terms_to_match is not None:
              instance['more_like_this']['percent_terms_to_match'] = percent_terms_to_match
          if min_term_freq is not None:
              instance['more_like_this']['min_term_freq'] = min_term_freq
          if max_query_terms is not None:
              instance['more_like_this']['max_query_terms'] = max_query_terms
          if stop_words is not None:
              instance['more_like_this']['stop_words'] = stop_words
          if min_doc_freq is not None:
              instance['more_like_this']['min_doc_freq'] = min_doc_freq
          if max_doc_freq is not None:
              instance['more_like_this']['max_doc_freq'] = max_doc_freq
          if min_word_len is not None:
              instance['more_like_this']['min_word_len'] = min_word_len
          if max_word_len is not None:
              instance['more_like_this']['max_word_len'] = max_word_len
          if boost_terms is not None:
              instance['more_like_this']['boost_terms'] = boost_terms
          if boost is not None:
              instance['more_like_this']['boost'] = boost
          if analyzer is not None:
              instance['more_like_this']['analyzer'] = analyzer
          return instance