code: def filter_data_by_lang_count(
      data: List[Tuple[str, str]],
      min_obs: int,
  ) -> List[Tuple[str, str]]:
      """
      Args:
          data
          min_obs
      """
      valid_langs = {
          lang
          for lang, count in collections.Counter(
              lang for _, lang in data
          ).most_common()
          if count >= min_obs
      }
      return [
          text_lang for text_lang in data if text_lang[1] in valid_langs
      ]
  
imports: from __future__ import annotations
  import argparse
  import collections
  import logging
  import operator
  import pathlib
  import random
  import statistics
  from typing import List, Tuple
  import sklearn.metrics
  import sklearn.model_selection
  import sklearn.preprocessing
  import thinc.api
  from cytoolz import itertoolz
  import textacy
  import textacy.datasets
  import textacy.lang_id_
  import textacy.lang_id_._datasets
  import textacy.lang_id_._training
  import textacy.preprocessing
label_desc: Use image node_id 6 for calling a global function with example usage: filter_data_by_lang_count(data, min_obs) and returns: unknown