class_docstr: Instantiated model of the TextMatcher.
  
  This is the instantiated model of the :class:`.TextMatcher`. For training
  your own model, please see the documentation of that class.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT, TOKEN``    ``CHUNK``
  ====================== ======================
  
  Parameters
  ----------
  mergeOverlapping
      Whether to merge overlapping matched chunks, by default False
  entityValue
      Value for the entity metadata field
  buildFromTokens
      Whether the TextMatcher should take the CHUNK from TOKEN or not
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docstr: Downloads and loads a pretrained model.
  
  Parameters
  ----------
  name : str, optional
      Name of the pretrained model
  lang : str, optional
      Language of the pretrained model, by default "en"
  remote_loc : str, optional
      Optional remote address of the resource, by default None. Will use
      Spark NLPs repositories otherwise.
  
  Returns
  -------
  TextMatcherModel
      The restored model
docstr_tok: ["Downloads","and","loads","a","pretrained","model",".","Parameters","--","--","--","--","--","name",":","str",",","optional","Name","of","the","pretrained","model","lang",":","str",",","optional","Language","of","the","pretrained","model",",","by","default","``","en","''","remote_loc",":","str",",","optional","Optional","remote","address","of","the","resource",",","by","default","None",".","Will","use","Spark","NLPs","repositories","otherwise",".","Returns","--","--","--","-","TextMatcherModel","The","restored","model"]
code: def pretrained(name, lang="en", remote_loc=None):
      """Downloads and loads a pretrained model.
  
      Parameters
      ----------
      name : str, optional
          Name of the pretrained model
      lang : str, optional
          Language of the pretrained model, by default "en"
      remote_loc : str, optional
          Optional remote address of the resource, by default None. Will use
          Spark NLPs repositories otherwise.
  
      Returns
      -------
      TextMatcherModel
          The restored model
      """
      from sparknlp.pretrained import ResourceDownloader
  
      return ResourceDownloader.downloadModel(
          TextMatcherModel, name, lang, remote_loc
      )
  
file_path: spark-nlp/python/sparknlp/annotator/matcher/text_matcher.py
imports: from sparknlp.common import *
label_desc: Use image node_id 5 for calling the TextMatcherModel obj's underlying member method code with example usage: obj.pretrained(name, lang, remote_loc) and returns: ResourceDownloader