code: def array_summary_stats(arr: Union[Column, str]) -> Column:
      """
      Computes the minimum, maximum, mean, standard deviation for an array of numerics.
  
      Added in version 0.3.0.
  
      Examples:
          >>> df = spark.createDataFrame([Row(arr=[1, 2, 3])])
          >>> df.select(glow.expand_struct(glow.array_summary_stats('arr'))).collect()
          [Row(mean=2.0, stdDev=1.0, min=1.0, max=3.0)]
  
      Args:
          arr : An array of any numeric type
  
      Returns:
          A struct containing double ``mean``, ``stdDev``, ``min``, and ``max`` fields
      """
      assert check_argument_types()
      output = Column(
          sc()._jvm.io.projectglow.functions.array_summary_stats(
              _to_java_column(arr)
          )
      )
      assert check_return_type(output)
      return output
  
imports: from pyspark import SparkContext
  from pyspark.sql.column import Column, _to_java_column, _to_seq
  from typeguard import check_argument_types, check_return_type
  from typing import Union
label_desc: Use image node_id 3 for calling a global function with example usage: array_summary_stats(arr) and returns: output