class_docstr: NLP class for default processing.
class_docstr_tok: ["NLP","class","for","default","processing","."]
docstr: Constructor for CLTK class.
  
  Args:
      language: ISO code
      custom_pipeline: Optional ``Pipeline`` for processing text.
  
  
  >>> from cltk import NLP
  >>> cltk_nlp = NLP(language="lat", suppress_banner=True)
  >>> isinstance(cltk_nlp, NLP)
  True
  >>> from cltk.core.data_types import Pipeline
  >>> from cltk.tokenizers import LatinTokenizationProcess
  >>> from cltk.languages.utils import get_lang
  >>> a_pipeline = Pipeline(description="A custom Latin pipeline", processes=[LatinTokenizationProcess], language=get_lang("lat"))
  >>> nlp = NLP(language="lat", custom_pipeline=a_pipeline, suppress_banner=True)
  >>> nlp.pipeline is a_pipeline
  True
docstr_tok: ["Constructor","for","CLTK","class",".","Args",":","language",":","ISO","code","custom_pipeline",":","Optional","``","Pipeline","``","for","processing","text",".",">",">",">","from","cltk","import","NLP",">",">",">","cltk_nlp","=","NLP","(","language=","''","lat","''",",","suppress_banner=True",")",">",">",">","isinstance","(","cltk_nlp",",","NLP",")","True",">",">",">","from","cltk.core.data_types","import","Pipeline",">",">",">","from","cltk.tokenizers","import","LatinTokenizationProcess",">",">",">","from","cltk.languages.utils","import","get_lang",">",">",">","a_pipeline","=","Pipeline","(","description=","''","A","custom","Latin","pipeline","''",",","processes=","[","LatinTokenizationProcess","]",",","language=get_lang","(","``","lat","''",")",")",">",">",">","nlp","=","NLP","(","language=","''","lat","''",",","custom_pipeline=a_pipeline",",","suppress_banner=True",")",">",">",">","nlp.pipeline","is","a_pipeline","True"]
code: def __init__(
      self,
      language: str,
      custom_pipeline: Pipeline = None,
      suppress_banner: bool = False,
  ) -> None:
      """Constructor for CLTK class.
  
      Args:
          language: ISO code
          custom_pipeline: Optional ``Pipeline`` for processing text.
  
  
      >>> from cltk import NLP
      >>> cltk_nlp = NLP(language="lat", suppress_banner=True)
      >>> isinstance(cltk_nlp, NLP)
      True
      >>> from cltk.core.data_types import Pipeline
      >>> from cltk.tokenizers import LatinTokenizationProcess
      >>> from cltk.languages.utils import get_lang
      >>> a_pipeline = Pipeline(description="A custom Latin pipeline", processes=[LatinTokenizationProcess], language=get_lang("lat"))
      >>> nlp = NLP(language="lat", custom_pipeline=a_pipeline, suppress_banner=True)
      >>> nlp.pipeline is a_pipeline
      True
      """
      self.language: Language = get_lang(language)
      self.pipeline = (
          custom_pipeline if custom_pipeline else self._get_pipeline()
      )
      if not suppress_banner:
          self._print_cltk_info()
          self._print_pipelines_for_current_lang()
          self._print_special_authorship_messages_for_current_lang()
          self._print_suppress_reminder()
  
file_path: cltk/src/cltk/nlp.py
imports: from threading import Lock
  from typing import List, Type
  import cltk
  from cltk.core.data_types import Doc, Language, Pipeline, Process
  from cltk.core.exceptions import UnimplementedAlgorithmError
  from cltk.languages.pipelines import AkkadianPipeline, ArabicPipeline, AramaicPipeline, ChinesePipeline, CopticPipeline, GothicPipeline, GreekPipeline, HindiPipeline, LatinPipeline, MiddleEnglishPipeline, MiddleFrenchPipeline, MiddleHighGermanPipeline, OCSPipeline, OldEnglishPipeline, OldFrenchPipeline, OldNorsePipeline, PaliPipeline, PanjabiPipeline, SanskritPipeline
  from cltk.languages.utils import get_lang
label_desc: Use image node_id 1 to create a new NLP object  with example: obj = NLP(language, custom_pipeline, suppress_banner)