class_docstr: Converts ``DOCUMENT`` type annotations into ``CHUNK`` type with the
  contents of a ``chunkCol``.
  
  Chunk text must be contained within input ``DOCUMENT``. May be either
  ``StringType`` or ``ArrayType[StringType]`` (using setIsArray). Useful for
  annotators that require a CHUNK type input.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT``           ``CHUNK``
  ====================== ======================
  
  Parameters
  ----------
  chunkCol
      Column that contains the string. Must be part of DOCUMENT
  startCol
      Column that has a reference of where the chunk begins
  startColByTokenIndex
      Whether start column is prepended by whitespace tokens
  isArray
      Whether the chunkCol is an array of strings, by default False
  failOnMissing
      Whether to fail the job if a chunk is not found within document.
      Return empty otherwise
  lowerCase
      Whether to lower case for matching case
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.common import *
  >>> from sparknlp.annotator import *
  >>> from sparknlp.training import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
  >>> chunkAssembler = Doc2Chunk() \
  ...     .setInputCols("document") \
  ...     .setChunkCol("target") \
  ...     .setOutputCol("chunk") \
  ...     .setIsArray(True)
  >>> data = spark.createDataFrame([[
  ...     "Spark NLP is an open-source text processing library for advanced natural language processing.",
  ...     ["Spark NLP", "text processing library", "natural language processing"]
  ... ]]).toDF("text", "target")
  >>> pipeline = Pipeline().setStages([documentAssembler, chunkAssembler]).fit(data)
  >>> result = pipeline.transform(data)
  >>> result.selectExpr("chunk.result", "chunk.annotatorType").show(truncate=False)
  +-----------------------------------------------------------------+---------------------+
  |result                                                           |annotatorType        |
  +-----------------------------------------------------------------+---------------------+
  |[Spark NLP, text processing library, natural language processing]|[chunk, chunk, chunk]|
  +-----------------------------------------------------------------+---------------------+
  
  See Also
  --------
  Chunk2Doc : for converting `CHUNK` annotations to `DOCUMENT`
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docstr: Sets whether to lower case for matching case.
  
  Parameters
  ----------
  value : bool
      Name of the Id Column
docstr_tok: ["Sets","whether","to","lower","case","for","matching","case",".","Parameters","--","--","--","--","--","value",":","bool","Name","of","the","Id","Column"]
code: def setLowerCase(self, value):
      """Sets whether to lower case for matching case.
  
      Parameters
      ----------
      value : bool
          Name of the Id Column
      """
      return self._set(lowerCase=value)
  
file_path: spark-nlp/python/sparknlp/base/doc2_chunk.py
imports: from pyspark import keyword_only
  from pyspark.ml.param import TypeConverters, Params, Param
  from sparknlp.internal import AnnotatorTransformer
  from sparknlp.common import AnnotatorProperties, AnnotatorType
label_desc: Use image node_id 8 for calling the Doc2Chunk obj's underlying member method code with example usage: obj.setLowerCase(value) and returns: self