class_docstr: Represents the output of Spark NLP Annotators and their details.
  
  Parameters
  ----------
  annotator_type : str
      The type of the output of the annotator. Possible values are ``DOCUMENT,
      TOKEN, WORDPIECE, WORD_EMBEDDINGS, SENTENCE_EMBEDDINGS, CATEGORY, DATE,
      ENTITY, SENTIMENT, POS, CHUNK, NAMED_ENTITY, NEGEX, DEPENDENCY,
      LABELED_DEPENDENCY, LANGUAGE, KEYWORD, DUMMY``.
  begin : int
      The index of the first character under this annotation.
  end : int
      The index of the last character under this annotation.
  result : str
      The resulting string of the annotation.
  metadata : dict
      Associated metadata for this annotation
  embeddings : list
      Embeddings vector where applicable
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docstr: Transforms an Annotation to a Spark `Row`.
  
  Parameters
  ----------
  annotation : Annotation
      The Annotation to be transformed.
  
  Returns
  -------
  :class:`pyspark.sql.Row`
      The new Row.
docstr_tok: ["Transforms","an","Annotation","to","a","Spark","`","Row","`",".","Parameters","--","--","--","--","--","annotation",":","Annotation","The","Annotation","to","be","transformed",".","Returns","--","--","--","-",":","class",":","`","pyspark.sql.Row","`","The","new","Row","."]
code: def toRow(annotation):
      """Transforms an Annotation to a Spark `Row`.
  
      Parameters
      ----------
      annotation : Annotation
          The Annotation to be transformed.
  
      Returns
      -------
      :class:`pyspark.sql.Row`
          The new Row.
      """
      from pyspark.sql import Row
  
      return Row(
          annotation.annotatorType,
          annotation.begin,
          annotation.end,
          annotation.result,
          annotation.metadata,
          annotation.embeddings,
      )
  
file_path: spark-nlp/python/sparknlp/annotation.py
imports: from pyspark.sql.types import *
label_desc: Use image node_id 9 for calling the Annotation obj's underlying member method code with example usage: obj.toRow(annotation) and returns: Row