class_docstr: A class for Named-Entity Tagging with Stanford Tagger. The input is the paths to:
  
  - a model trained on training data
  - (optionally) the path to the stanford tagger jar file. If not specified here,
    then this jar file must be specified in the CLASSPATH environment variable.
  - (optionally) the encoding of the training data (default: UTF-8)
  
  Example:
  
      >>> from nltk.tag import StanfordNERTagger
      >>> st = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz') # doctest: +SKIP
      >>> st.tag('Rami Eid is studying at Stony Brook University in NY'.split()) # doctest: +SKIP
      [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'),
       ('at', 'O'), ('Stony', 'ORGANIZATION'), ('Brook', 'ORGANIZATION'),
       ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'LOCATION')]
code: def _cmd(self):
      # Adding -tokenizerFactory edu.stanford.nlp.process.WhitespaceTokenizer -tokenizerOptions tokenizeNLs=false for not using stanford Tokenizer
      return [
          "edu.stanford.nlp.ie.crf.CRFClassifier",
          "-loadClassifier",
          self._stanford_model,
          "-textFile",
          self._input_file_path,
          "-outputFormat",
          self._FORMAT,
          "-tokenizerFactory",
          "edu.stanford.nlp.process.WhitespaceTokenizer",
          "-tokenizerOptions",
          '"tokenizeNLs=false"',
      ]
  
imports: import os
  import tempfile
  import warnings
  from abc import abstractmethod
  from subprocess import PIPE
  from nltk.internals import _java_options, config_java, find_file, find_jar, java
  from nltk.tag.api import TaggerI
label_desc: Use image node_id 2 for calling the StanfordNERTagger obj's underlying member method code with example usage: obj._cmd() and returns: list