code: def apply_features(feature_func, toks, labeled=None):
      """
      Use the ``LazyMap`` class to construct a lazy list-like
      object that is analogous to ``map(feature_func, toks)``.  In
      particular, if ``labeled=False``, then the returned list-like
      object's values are equal to::
  
          [feature_func(tok) for tok in toks]
  
      If ``labeled=True``, then the returned list-like object's values
      are equal to::
  
          [(feature_func(tok), label) for (tok, label) in toks]
  
      The primary purpose of this function is to avoid the memory
      overhead involved in storing all the featuresets for every token
      in a corpus.  Instead, these featuresets are constructed lazily,
      as-needed.  The reduction in memory overhead can be especially
      significant when the underlying list of tokens is itself lazy (as
      is the case with many corpus readers).
  
      :param feature_func: The function that will be applied to each
          token.  It should return a featureset -- i.e., a dict
          mapping feature names to feature values.
      :param toks: The list of tokens to which ``feature_func`` should be
          applied.  If ``labeled=True``, then the list elements will be
          passed directly to ``feature_func()``.  If ``labeled=False``,
          then the list elements should be tuples ``(tok,label)``, and
          ``tok`` will be passed to ``feature_func()``.
      :param labeled: If true, then ``toks`` contains labeled tokens --
          i.e., tuples of the form ``(tok, label)``.  (Default:
          auto-detect based on types.)
      """
      if labeled is None:
          labeled = toks and isinstance(toks[0], (tuple, list))
      if labeled:
  
          def lazy_func(labeled_token):
              return (feature_func(labeled_token[0]), labeled_token[1])
  
          return LazyMap(lazy_func, toks)
      else:
          return LazyMap(feature_func, toks)
  
file_path: nltk/nltk/classify/util.py
imports: import math
  import nltk.classify.util
  from nltk.util import LazyMap
label_desc: Use image node_id 1 for calling a global function with example usage: apply_features(feature_func, toks, labeled) and returns: LazyMap, LazyMap, feature_func,  labeled_token