code: def make_sparse2dense(df, col_name: str, possible_keys: List):
      """Given a list of possible keys, convert sparse map to dense array.
      In our example, both value_type is assumed to be a float.
      """
      output_type = StructType(
          [
              StructField("presence", ArrayType(BooleanType()), False),
              StructField("dense", ArrayType(FloatType()), False),
          ]
      )
  
      def sparse2dense(map_col):
          assert isinstance(
              map_col, dict
          ), f"{map_col} has type {type(map_col)} and is not a dict."
          presence = []
          dense = []
          for key in possible_keys:
              val = map_col.get(key, None)
              if val is not None:
                  presence.append(True)
                  dense.append(float(val))
              else:
                  presence.append(False)
                  dense.append(0.0)
          return presence, dense
  
      sparse2dense_udf = udf(sparse2dense, output_type)
      df = df.withColumn(col_name, sparse2dense_udf(col_name))
      df = df.withColumn(
          f"{col_name}_presence", col(f"{col_name}.presence")
      )
      df = df.withColumn(col_name, col(f"{col_name}.dense"))
      return df
  
imports: import logging
  from typing import List, Optional, Tuple
  from pyspark.sql.functions import col, crc32, explode, map_keys, udf
  from pyspark.sql.types import ArrayType, BooleanType, FloatType, LongType, MapType, StructField, StructType
  from reagent.data.data_fetcher import DataFetcher
  from reagent.data.spark_utils import get_spark_session, get_table_url
  from reagent.workflow.types import Dataset, TableSpec
label_desc: Use image node_id 5 for calling a global function with example usage: make_sparse2dense(df, col_name, possible_keys) and returns: df, presence,  dense