class_docstr: Loads a Represents a fully constructed and trained Spark NLP pipeline,
  ready to be used.
  
  This way, a whole pipeline can be defined in 1 line. Additionally, the
  :class:`.LightPipeline` version of the model can be retrieved with member
  :attr:`.light_model`.
  
  For more extended examples see the `Pipelines page
  <https://sparknlp.org/docs/en/pipelines>`_ and our `Github Model
  Repository <https://github.com/JohnSnowLabs/spark-nlp-models>`_  for
  available pipeline models.
  
  Parameters
  ----------
  name : str
      Name of the PretrainedPipeline. These can be gathered from the Pipelines
      Page.
  lang : str, optional
      Langauge of the model, by default 'en'
  remote_loc : str, optional
      Link to the remote location of the model (if it was already downloaded),
      by default None
  parse_embeddings : bool, optional
      Whether to parse embeddings, by default False
  disk_location : str , optional
      Path to locally stored PretrainedPipeline, by default None
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docstr: Annotates the data provided into `Annotation` type results.
  
  The data should be either a list or a str.
  
  Parameters
  ----------
  target : list or str
      The data to be annotated
  
  Returns
  -------
  List[Annotation]
      The result of the annotation
  
  Examples
  --------
  >>> from sparknlp.pretrained import PretrainedPipeline
  >>> explain_document_pipeline = PretrainedPipeline("explain_document_dl")
  >>> result = explain_document_pipeline.fullAnnotate('U.N. official Ekeus heads for Baghdad.')
  >>> result[0].keys()
  dict_keys(['entities', 'stem', 'checked', 'lemma', 'document', 'pos', 'token', 'ner', 'embeddings', 'sentence'])
  >>> result[0]["ner"]
  [Annotation(named_entity, 0, 2, B-ORG, {'word': 'U.N'}),
  Annotation(named_entity, 3, 3, O, {'word': '.'}),
  Annotation(named_entity, 5, 12, O, {'word': 'official'}),
  Annotation(named_entity, 14, 18, B-PER, {'word': 'Ekeus'}),
  Annotation(named_entity, 20, 24, O, {'word': 'heads'}),
  Annotation(named_entity, 26, 28, O, {'word': 'for'}),
  Annotation(named_entity, 30, 36, B-LOC, {'word': 'Baghdad'}),
  Annotation(named_entity, 37, 37, O, {'word': '.'})]
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code: def fullAnnotate(self, target, optional_target=""):
      """Annotates the data provided into `Annotation` type results.
  
      The data should be either a list or a str.
  
      Parameters
      ----------
      target : list or str
          The data to be annotated
  
      Returns
      -------
      List[Annotation]
          The result of the annotation
  
      Examples
      --------
      >>> from sparknlp.pretrained import PretrainedPipeline
      >>> explain_document_pipeline = PretrainedPipeline("explain_document_dl")
      >>> result = explain_document_pipeline.fullAnnotate('U.N. official Ekeus heads for Baghdad.')
      >>> result[0].keys()
      dict_keys(['entities', 'stem', 'checked', 'lemma', 'document', 'pos', 'token', 'ner', 'embeddings', 'sentence'])
      >>> result[0]["ner"]
      [Annotation(named_entity, 0, 2, B-ORG, {'word': 'U.N'}),
      Annotation(named_entity, 3, 3, O, {'word': '.'}),
      Annotation(named_entity, 5, 12, O, {'word': 'official'}),
      Annotation(named_entity, 14, 18, B-PER, {'word': 'Ekeus'}),
      Annotation(named_entity, 20, 24, O, {'word': 'heads'}),
      Annotation(named_entity, 26, 28, O, {'word': 'for'}),
      Annotation(named_entity, 30, 36, B-LOC, {'word': 'Baghdad'}),
      Annotation(named_entity, 37, 37, O, {'word': '.'})]
      """
      annotations = self.light_model.fullAnnotate(
          target, optional_target
      )
      return annotations
  
file_path: spark-nlp/python/sparknlp/pretrained/pretrained_pipeline.py
imports: from pyspark.ml import PipelineModel
  from pyspark.sql import DataFrame
  from sparknlp.base import LightPipeline
  from sparknlp.pretrained.resource_downloader import ResourceDownloader
label_desc: Use image node_id 4 for calling the PretrainedPipeline obj's underlying member method code with example usage: obj.fullAnnotate(target, optional_target) and returns: annotations