class_docstr: Annotator that detects sentence boundaries using a deep learning approach.
  
  Instantiated Model of the :class:`.SentenceDetectorDLApproach`.
  Detects sentence boundaries using a deep learning approach.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> sentenceDL = SentenceDetectorDLModel.pretrained() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentencesDL")
  
  The default model is ``"sentence_detector_dl"``, if no name is provided. For
  available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Sentence+Detection>`__.
  
  Each extracted sentence can be returned in an Array or exploded to separate
  rows, if ``explodeSentences`` is set to ``true``.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/multilingual/SentenceDetectorDL.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT``           ``DOCUMENT``
  ====================== ======================
  
  Parameters
  ----------
  modelArchitecture
      Model architecture (CNN)
  explodeSentences
      whether to explode each sentence into a different row, for better
      parallelization. Defaults to false.
  customBounds
      characters used to explicitly mark sentence bounds, by default []
  useCustomBoundsOnly
      Only utilize custom bounds in sentence detection, by default False
  splitLength
      length at which sentences will be forcibly split
  minLength
      Set the minimum allowed length for each sentence, by default 0
  maxLength
      Set the maximum allowed length for each sentence, by default 99999
  impossiblePenultimates
      Impossible penultimates - list of strings which a sentence can't end
      with
  
  Examples
  --------
  In this example, the normal `SentenceDetector` is compared to the
  `SentenceDetectorDLModel`. In a pipeline, `SentenceDetectorDLModel` can be
  used as a replacement for the `SentenceDetector`.
  
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentences")
  >>> sentenceDL = SentenceDetectorDLModel \
  ...     .pretrained("sentence_detector_dl", "en") \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentencesDL")
  >>> pipeline = Pipeline().setStages([
  ...     documentAssembler,
  ...     sentence,
  ...     sentenceDL
  ... ])
  >>> data = spark.createDataFrame([["""John loves Mary.Mary loves Peter
  ...     Peter loves Helen .Helen loves John;
  ...     Total: four people involved."""]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(sentences.result) as sentences").show(truncate=False)
  +----------------------------------------------------------+
  |sentences                                                 |
  +----------------------------------------------------------+
  |John loves Mary.Mary loves Peter\n     Peter loves Helen .|
  |Helen loves John;                                         |
  |Total: four people involved.                              |
  +----------------------------------------------------------+
  >>> result.selectExpr("explode(sentencesDL.result) as sentencesDL").show(truncate=False)
  +----------------------------+
  |sentencesDL                 |
  +----------------------------+
  |John loves Mary.            |
  |Mary loves Peter            |
  |Peter loves Helen .         |
  |Helen loves John;           |
  |Total: four people involved.|
  +----------------------------+
docstr: Downloads and loads a pretrained model.
  
  Parameters
  ----------
  name : str, optional
      Name of the pretrained model, by default "sentence_detector_dl"
  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
  -------
  SentenceDetectorDLModel
      The restored model
docstr_tok: ["Downloads","and","loads","a","pretrained","model",".","Parameters","--","--","--","--","--","name",":","str",",","optional","Name","of","the","pretrained","model",",","by","default","``","sentence_detector_dl","''","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","--","--","--","-","SentenceDetectorDLModel","The","restored","model"]
code: def pretrained(
      name="sentence_detector_dl", lang="en", remote_loc=None
  ):
      """Downloads and loads a pretrained model.
  
      Parameters
      ----------
      name : str, optional
          Name of the pretrained model, by default "sentence_detector_dl"
      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
      -------
      SentenceDetectorDLModel
          The restored model
      """
      from sparknlp.pretrained import ResourceDownloader
  
      return ResourceDownloader.downloadModel(
          SentenceDetectorDLModel, name, lang, remote_loc
      )
  
imports: from sparknlp.common import *
label_desc: Use image node_id 10 for calling the SentenceDetectorDLModel obj's underlying member method code with example usage: obj.pretrained(name, lang, remote_loc) and returns: ResourceDownloader