class_docstr: Creates a LightPipeline from a Spark PipelineModel.
  
  LightPipeline is a Spark NLP specific Pipeline class equivalent to Spark
  ML Pipeline. The difference is that it’s execution does not hold to
  Spark principles, instead it computes everything locally (but in
  parallel) in order to achieve fast results when dealing with small
  amounts of data. This means, we do not input a Spark Dataframe, but a
  string or an Array of strings instead, to be annotated. To create Light
  Pipelines, you need to input an already trained (fit) Spark ML Pipeline.
  
  It’s :meth:`.transform` has now an alternative :meth:`.annotate`, which
  directly outputs the results.
  
  Parameters
  ----------
  pipelineModel : :class:`pyspark.ml.PipelineModel`
      The PipelineModel containing Spark NLP Annotators
  parse_embeddings : bool, optional
      Whether to parse embeddings, by default False
  
  Notes
  -----
  Use :meth:`.fullAnnotate` to also output the result as
  :class:`.Annotation`, with metadata.
  
  Examples
  --------
  >>> from sparknlp.base import LightPipeline
  >>> light = LightPipeline(pipeline.fit(data))
  >>> light.annotate("We are very happy about Spark NLP")
  {
      'document': ['We are very happy about Spark NLP'],
      'lemmas': ['We', 'be', 'very', 'happy', 'about', 'Spark', 'NLP'],
      'pos': ['PRP', 'VBP', 'RB', 'JJ', 'IN', 'NNP', 'NNP'],
      'sentence': ['We are very happy about Spark NLP'],
      'spell': ['We', 'are', 'very', 'happy', 'about', 'Spark', 'NLP'],
      'stems': ['we', 'ar', 'veri', 'happi', 'about', 'spark', 'nlp'],
      'token': ['We', 'are', 'very', 'happy', 'about', 'Spark', 'NLP']
  }
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code: def __fullAnnotateQuestionAnswering(self, question, context):
      result = []
      if type(question) is str and type(context) is str:
          annotations_dict = self._lightPipeline.fullAnnotateJava(
              question, context
          )
          result.append(self.__buildStages(annotations_dict))
      else:
          full_annotations = self._lightPipeline.fullAnnotateJava(
              question, context
          )
          for annotations_dict in full_annotations:
              result.append(self.__buildStages(annotations_dict))
  
      return result
  
imports: import sparknlp.internal
  from sparknlp.annotation import Annotation
  from sparknlp.annotation_audio import AnnotationAudio
  from sparknlp.annotation_image import AnnotationImage
  from sparknlp.common import AnnotatorApproach, AnnotatorModel
  from sparknlp.internal import AnnotatorTransformer
label_desc: Use image node_id 14 for calling the LightPipeline obj's underlying member method code with example usage: obj.__fullAnnotateQuestionAnswering(question, context) and returns: result