class_docstr: This annotator utilizes WordEmbeddings, BertEmbeddings etc. to generate
  chunk embeddings from either Chunker, NGramGenerator, or NerConverter
  outputs.
  
  For extended examples of usage, see the `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb>`__.
  
  ========================== ======================
  Input Annotation types     Output Annotation type
  ========================== ======================
  ``CHUNK, WORD_EMBEDDINGS`` ``WORD_EMBEDDINGS``
  ========================== ======================
  
  Parameters
  ----------
  poolingStrategy
      Choose how you would like to aggregate Word Embeddings to Chunk
      Embeddings, by default AVERAGE.
      Possible Values: ``AVERAGE, SUM``
  skipOOV
      Whether to discard default vectors for OOV words from the
      aggregation/pooling.
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  
  Extract the Embeddings from the NGrams
  
  >>> documentAssembler = DocumentAssembler() \
  ...     .setInputCol("text") \
  ...     .setOutputCol("document")
  >>> sentence = SentenceDetector() \
  ...     .setInputCols(["document"]) \
  ...     .setOutputCol("sentence")
  >>> tokenizer = Tokenizer() \
  ...     .setInputCols(["sentence"]) \
  ...     .setOutputCol("token")
  >>> nGrams = NGramGenerator() \
  ...     .setInputCols(["token"]) \
  ...     .setOutputCol("chunk") \
  ...     .setN(2)
  >>> embeddings = WordEmbeddingsModel.pretrained() \
  ...     .setInputCols(["sentence", "token"]) \
  ...     .setOutputCol("embeddings") \
  ...     .setCaseSensitive(False)
  
  Convert the NGram chunks into Word Embeddings
  
  >>> chunkEmbeddings = ChunkEmbeddings() \
  ...     .setInputCols(["chunk", "embeddings"]) \
  ...     .setOutputCol("chunk_embeddings") \
  ...     .setPoolingStrategy("AVERAGE")
  >>> pipeline = Pipeline() \
  ...     .setStages([
  ...       documentAssembler,
  ...       sentence,
  ...       tokenizer,
  ...       nGrams,
  ...       embeddings,
  ...       chunkEmbeddings
  ...     ])
  >>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
  >>> result = pipeline.fit(data).transform(data)
  >>> result.selectExpr("explode(chunk_embeddings) as result") \
  ...     .select("result.annotatorType", "result.result", "result.embeddings") \
  ...     .show(5, 80)
  +---------------+----------+--------------------------------------------------------------------------------+
  |  annotatorType|    result|                                                                      embeddings|
  +---------------+----------+--------------------------------------------------------------------------------+
  |word_embeddings|   This is|[-0.55661, 0.42829502, 0.86661, -0.409785, 0.06316501, 0.120775, -0.0732005, ...|
  |word_embeddings|      is a|[-0.40674996, 0.22938299, 0.50597, -0.288195, 0.555655, 0.465145, 0.140118, 0...|
  |word_embeddings|a sentence|[0.17417, 0.095253006, -0.0530925, -0.218465, 0.714395, 0.79860497, 0.0129999...|
  |word_embeddings|sentence .|[0.139705, 0.177955, 0.1887775, -0.45545, 0.20030999, 0.461557, -0.07891501, ...|
  +---------------+----------+--------------------------------------------------------------------------------+
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docstr: Sets whether to discard default vectors for OOV words from the
  aggregation/pooling.
  
  Parameters
  ----------
  value : bool
      whether to discard default vectors for OOV words from the
      aggregation/pooling.
docstr_tok: ["Sets","whether","to","discard","default","vectors","for","OOV","words","from","the","aggregation\/pooling",".","Parameters","--","--","--","--","--","value",":","bool","whether","to","discard","default","vectors","for","OOV","words","from","the","aggregation\/pooling","."]
code: def setSkipOOV(self, value):
      """Sets whether to discard default vectors for OOV words from the
      aggregation/pooling.
  
      Parameters
      ----------
      value : bool
          whether to discard default vectors for OOV words from the
          aggregation/pooling.
      """
      return self._set(skipOOV=value)
  
imports: from sparknlp.common import *
label_desc: Use image node_id 3 for calling the ChunkEmbeddings obj's underlying member method code with example usage: obj.setSkipOOV(value) and returns: self