code: def min_max_scale(df, col):
      assembler = VectorAssembler(
          inputCols=[col], outputCol=col + "_vec"
      )
      scaler = MinMaxScaler(
          inputCol=col + "_vec", outputCol=col + "_scaled"
      )
      pipeline = Pipeline(stages=[assembler, scaler])
      scalerModel = pipeline.fit(df)
      df = scalerModel.transform(df)
      df = df.drop(col, col + "_vec").withColumnRenamed(
          col + "_scaled", col
      )
      return df
  
file_path: BigDL/python/orca/tutorial/NCF/process_spark_dataframe.py
imports: import os
  from pyspark.ml.feature import StringIndexer, MinMaxScaler, VectorAssembler
  from pyspark.ml import Pipeline
  from pyspark.sql.types import StructField, StructType, IntegerType, LongType, ArrayType, StringType
  from pyspark.sql.functions import udf, lit, collect_list, explode, concat
  from bigdl.orca import OrcaContext
label_desc: Use image node_id 4 for calling a global function with example usage: min_max_scale(df, col) and returns: df