code: def map_annotations_col(
      dataframe: DataFrame,
      f,
      column: str,
      output_column: str,
      annotatyon_type: str,
      output_type: DataType = Annotation.arrayType(),
  ):
      """Creates a Spark UDF to map over a column of Annotation results.
  
      Parameters
      ----------
      dataframe : DataFrame
          Input DataFrame
      f : function
          Function to apply to the column
      column : str
          Name of the input column
      output_column : str
          Name of the output column
      annotatyon_type : str
          Annotator type
      output_type : DataType, optional
          Output type, by default Annotation.arrayType()
  
      Returns
      -------
      :class:`pyspark.sql.DataFrame`
          Transformed DataFrame
  
      Examples
      --------
      >>> from sparknlp.pretrained import PretrainedPipeline
      >>> from sparknlp.functions import *
      >>> explain_document_pipeline = PretrainedPipeline("explain_document_dl")
      >>> data = spark.createDataFrame([["U.N. official Ekeus heads for Baghdad."]]).toDF("text")
      >>> result = explain_document_pipeline.transform(data)
      >>> chunks_df = map_annotations_col(
      ...     result,
      ...     lambda x: [
      ...         Annotation("chunk", a.begin, a.end, a.result, a.metadata, a.embeddings)
      ...         for a in x
      ...     ],
      ...     "pos",
      ...     "pos_chunk",
      ...     "chunk",
      ... )
      >>> chunks_df.selectExpr("explode(pos_chunk)").show()
      +--------------------+
      |                 col|
      +--------------------+
      |[chunk, 0, 2, NNP...|
      |[chunk, 3, 3, ., ...|
      |[chunk, 5, 12, JJ...|
      |[chunk, 14, 18, N...|
      |[chunk, 20, 24, V...|
      |[chunk, 26, 28, I...|
      |[chunk, 30, 36, N...|
      |[chunk, 37, 37, ....|
      +--------------------+
      """
      return dataframe.withColumn(
          output_column,
          map_annotations(f, output_type)(column).alias(
              output_column, metadata={"annotatorType": annotatyon_type}
          ),
      )
  
file_path: spark-nlp/python/sparknlp/functions.py
imports: from pyspark.sql.functions import udf, array
  from pyspark.sql.types import *
  from pyspark.sql import DataFrame
  from sparknlp.annotation import Annotation
label_desc: Use image node_id 4 for calling a global function with example usage: map_annotations_col(dataframe, f, column, output_column, annotatyon_type, output_type) and returns: dataframe