class_docstr: Pipeline for handling multilingual data. Takes in text, detects language, and routes request to pipeline for that
  language.
  
  You can specify options to individual language pipelines with the lang_configs field.
  For example, if you want English pipelines to have NER, but want to turn that off for French, you can do:
      lang_configs = {"en": {"processors": "tokenize,pos,lemma,depparse,ner"},
                      "fr": {"processors": "tokenize,pos,lemma,depparse"}}
      pipeline = MultilingualPipeline(lang_configs=lang_configs)
  
  You can also pass in a defaultdict created in such a way that it provides default parameters for each language.
  For example, in order to only get tokenization for each language:
  (remembering that the Pipeline will automagically add MWT to a language which uses MWT):
      from collections import defaultdict
      lang_configs = defaultdict(lambda: dict(processors="tokenize"))
      pipeline = MultilingualPipeline(lang_configs=lang_configs)
  
  download_method can be set as in Pipeline to turn off downloading
    of the .json config or turn off downloading of everything
code: def __call__(self, doc):
      doc = self.process(doc)
      return doc
  
file_path: stanza/stanza/pipeline/multilingual.py
imports: from collections import OrderedDict
  import copy
  import logging
  from typing import Union
  from stanza.models.common.doc import Document
  from stanza.models.common.utils import default_device
  from stanza.pipeline.core import Pipeline, DownloadMethod
  from stanza.pipeline._constants import *
  from stanza.resources.common import DEFAULT_MODEL_DIR, get_language_resources, load_resources_json
label_desc: Use image node_id 4 for calling the MultilingualPipeline obj's underlying member method code with example usage: obj.__call__(doc) and returns: doc