class_docstr: Prepares data into a format that is processable by Spark NLP.
  
  This is the entry point for every Spark NLP pipeline. The
  `DocumentAssembler` reads ``String`` columns. Additionally,
  :meth:`.setCleanupMode` can be used to pre-process the
  text (Default: ``disabled``). For possible options please refer the
  parameters section.
  
  For more extended examples on document pre-processing see the
  `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``NONE``               ``DOCUMENT``
  ====================== ======================
  
  Parameters
  ----------
  inputCol
      Input column name
  outputCol
      Output column name
  idCol
      Name of String type column for row id.
  metadataCol
      Name of Map type column with metadata information
  cleanupMode
      How to cleanup the document , by default disabled.
      Possible values: ``disabled, inplace, inplace_full, shrink, shrink_full,
      each, each_full, delete_full``
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from pyspark.ml import Pipeline
  >>> data = spark.createDataFrame([["Spark NLP is an open-source text processing library."]]).toDF("text")
  >>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
  >>> result = documentAssembler.transform(data)
  >>> result.select("document").show(truncate=False)
  +----------------------------------------------------------------------------------------------+
  |document                                                                                      |
  +----------------------------------------------------------------------------------------------+
  |[[document, 0, 51, Spark NLP is an open-source text processing library., [sentence -> 0], []]]|
  +----------------------------------------------------------------------------------------------+
  >>> result.select("document").printSchema()
  root
  |-- document: array (nullable = True)
  |    |-- element: struct (containsNull = True)
  |    |    |-- annotatorType: string (nullable = True)
  |    |    |-- begin: integer (nullable = False)
  |    |    |-- end: integer (nullable = False)
  |    |    |-- result: string (nullable = True)
  |    |    |-- metadata: map (nullable = True)
  |    |    |    |-- key: string
  |    |    |    |-- value: string (valueContainsNull = True)
  |    |    |-- embeddings: array (nullable = True)
  |    |    |    |-- element: float (containsNull = False)
docstr: Sets how to cleanup the document, by default disabled.
  Possible values: ``disabled, inplace, inplace_full, shrink, shrink_full,
  each, each_full, delete_full``
  
  Parameters
  ----------
  value : str
      Cleanup mode
docstr_tok: ["Sets","how","to","cleanup","the","document",",","by","default","disabled",".","Possible","values",":","``","disabled",",","inplace",",","inplace_full",",","shrink",",","shrink_full",",","each",",","each_full",",","delete_full","``","Parameters","--","--","--","--","--","value",":","str","Cleanup","mode"]
code: def setCleanupMode(self, value):
      """Sets how to cleanup the document, by default disabled.
      Possible values: ``disabled, inplace, inplace_full, shrink, shrink_full,
      each, each_full, delete_full``
  
      Parameters
      ----------
      value : str
          Cleanup mode
      """
      if value.strip().lower() not in [
          "disabled",
          "inplace",
          "inplace_full",
          "shrink",
          "shrink_full",
          "each",
          "each_full",
          "delete_full",
      ]:
          raise Exception(
              "Cleanup mode possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full"
          )
      return self._set(cleanupMode=value)
  
file_path: spark-nlp/python/sparknlp/base/document_assembler.py
imports: from pyspark import keyword_only
  from pyspark.ml.param import TypeConverters, Params, Param
  from sparknlp.common import AnnotatorType
  from sparknlp.internal import AnnotatorTransformer
label_desc: Use image node_id 7 for calling the DocumentAssembler obj's underlying member method code with example usage: obj.setCleanupMode(value) and returns: self