class_docstr: A processing interface for assigning a tag to each token in a list.
  Tags are case sensitive strings that identify some property of each
  token, such as its part of speech or its sense.
  
  Some taggers require specific types for their tokens.  This is
  generally indicated by the use of a sub-interface to ``TaggerI``.
  For example, featureset taggers, which are subclassed from
  ``FeaturesetTagger``, require that each token be a ``featureset``.
  
  Subclasses must define:
    - either ``tag()`` or ``tag_sents()`` (or both)
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code: def _check_params(self, train, model):
      if (train and model) or (not train and not model):
          raise ValueError(
              "Must specify either training data or trained model."
          )
  
imports: from abc import ABCMeta, abstractmethod
  from functools import lru_cache
  from itertools import chain
  from typing import Dict
  from nltk.internals import deprecated, overridden
  from nltk.metrics import ConfusionMatrix, accuracy
  from nltk.tag.util import untag
label_desc: Use image node_id 11 for calling the TaggerI obj's underlying member method code with example usage: obj._check_params(train, model) without return types