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
  ----------
  path_to_image : list or str
      Source path of image, list of paths to images
  
  Returns
  -------
  List[AnnotationImage]
      The result of the annotation
docstr_tok: ["Annotates","the","data","provided","into","`","Annotation","`","type","results",".","The","data","should","be","either","a","list","or","a","str",".","Parameters","--","--","--","--","--","path_to_image",":","list","or","str","Source","path","of","image",",","list","of","paths","to","images","Returns","--","--","--","-","List","[","AnnotationImage","]","The","result","of","the","annotation"]
code: def fullAnnotateImage(self, path_to_image):
      """Annotates the data provided into `Annotation` type results.
  
      The data should be either a list or a str.
  
      Parameters
      ----------
      path_to_image : list or str
          Source path of image, list of paths to images
  
      Returns
      -------
      List[AnnotationImage]
          The result of the annotation
      """
      pipeline = self.light_model
      return pipeline.fullAnnotateImage(path_to_image)
  
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 5 for calling the PretrainedPipeline obj's underlying member method code with example usage: obj.fullAnnotateImage(path_to_image) and returns: pipeline