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: Sets the maximum allowed length for each sentence, by default
  99999
  
  Parameters
  ----------
  value : int
      Maximum allowed length for each sentence
docstr_tok: ["Sets","the","maximum","allowed","length","for","each","sentence",",","by","default","99999","Parameters","--","--","--","--","--","value",":","int","Maximum","allowed","length","for","each","sentence"]
code: def setMaxLength(self, value):
      """Sets the maximum allowed length for each sentence, by default
      99999
  
      Parameters
      ----------
      value : int
          Maximum allowed length for each sentence
      """
      return self._set(maxLength=value)
  
imports: from sparknlp.common import *
label_desc: Use image node_id 7 for calling the SentenceDetectorDLModel obj's underlying member method code with example usage: obj.setMaxLength(value) and returns: self