class_docstr: Tokenizes and flattens extracted NER chunks.
  
  The ChunkTokenizer will split the extracted NER ``CHUNK`` type Annotations
  and will create ``TOKEN`` type Annotations.
  The result is then flattened, resulting in a single array.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``CHUNK``              ``TOKEN``
  ====================== ======================
  
  Parameters
  ----------
  None
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> sparknlp.common import *
  >>> from pyspark.ml import Pipeline
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentenceDetector = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token")
  >>> entityExtractor = TextMatcher() \
  ...     .setInputCols(["sentence", "token"]) \
  ...     .setEntities("src/test/resources/entity-extractor/test-chunks.txt", ReadAs.TEXT) \
  ...     .setOutputCol("entity")
  >>> chunkTokenizer = ChunkTokenizer() \
  ...     .setInputCols(["entity"]) \
  ...     .setOutputCol("chunk_token")
  >>> pipeline = Pipeline().setStages([
  ...         documentAssembler,
  ...         sentenceDetector,
  ...         tokenizer,
  ...         entityExtractor,
  ...         chunkTokenizer
  ... ])
  >>> data = spark.createDataFrame([
  ...     ["Hello world, my name is Michael, I am an artist and I work at Benezar"],
  ...     ["Robert, an engineer from Farendell, graduated last year. The other one, Lucas, graduated last week."]
  >>> ]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("entity.result as entity" , "chunk_token.result as chunk_token").show(truncate=False)
  +-----------------------------------------------+---------------------------------------------------+
  |entity                                         |chunk_token                                        |
  +-----------------------------------------------+---------------------------------------------------+
  |[world, Michael, work at Benezar]              |[world, Michael, work, at, Benezar]                |
  |[engineer from Farendell, last year, last week]|[engineer, from, Farendell, last, year, last, week]|
  +-----------------------------------------------+---------------------------------------------------+
code: def __init__(self):
      super(Tokenizer, self).__init__(
          classname="com.johnsnowlabs.nlp.annotators.ChunkTokenizer"
      )
  
file_path: spark-nlp/python/sparknlp/annotator/token/chunk_tokenizer.py
imports: from sparknlp.common import *
  from sparknlp.annotator.token.tokenizer import Tokenizer, TokenizerModel
label_desc: Use image node_id 1 to create a new ChunkTokenizer object from inherited base classes: Tokenizer with example: obj = ChunkTokenizer()