class_docstr: Find contexts where more than one possible target value can
  appear.  E.g. if targets are word-initial letters, and contexts
  are the remainders of words, then we would like to find cases like
  "fat" vs "cat", and "training" vs "draining".  If targets are
  parts-of-speech and contexts are words, then we would like to find
  cases like wind (noun) 'air in rapid motion', vs wind (verb)
  'coil, wrap'.
docstr: Determine which contexts occurred with enough distinct targets.
  
  :param minimum: the minimum number of distinct target forms
  :type minimum: int
  :rtype: list
docstr_tok: ["Determine","which","contexts","occurred","with","enough","distinct","targets",".",":","param","minimum",":","the","minimum","number","of","distinct","target","forms",":","type","minimum",":","int",":","rtype",":","list"]
code: def contexts(self, minimum=2):
      """
      Determine which contexts occurred with enough distinct targets.
  
      :param minimum: the minimum number of distinct target forms
      :type minimum: int
      :rtype: list
      """
      return [
          c for c in self._contexts if len(self._seen[c]) >= minimum
      ]
  
file_path: nltk/nltk/misc/minimalset.py
imports: from collections import defaultdict
label_desc: Use image node_id 3 for calling the MinimalSet obj's underlying member method code with example usage: obj.contexts(minimum) and returns: unknown