spark data frame Schema With Data Definitions

I'm trying to add comments to the field (Schema With Data Definitions), below is the implementation I'm trying.

Tried to with StructType.add() (code in comments) and also with StructType([ StructField("filed",dtype,boolean,metadata )]

got below error. Not sure this implementation works, Can someone help me here, I'm new to spark.

I'm looking for output(Schema With Data Definitions) like

df.printSchema()

root
 |-- firstname: string (nullable = true) comments:val1
 |-- middlename: string (nullable = true) comments:val2
 |-- lastname: string (nullable = true) comments:val3
 |-- id: string (nullable = true) comments:val4
 |-- gender: string (nullable = true) comments:val5
 |-- salary: integer (nullable = true) comments:val6

error:

IllegalArgumentException: Failed to convert the JSON string '{"metadata":"val1","name":"firstname","nullable":true,"type":"string"}' to a field.

Code Which I'm trying to add comments to the field:

import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType,StructField, StringType, IntegerType

spark = SparkSession.builder.master("local[1]") \
                    .appName('SparkByExamples.com') \
                    .getOrCreate()

data = [("James","","Smith","36636","M",3000),
    ("Michael","Rose","","40288","M",4000),
    ("Robert","","Williams","42114","M",4000),
    ("Maria","Anne","Jones","39192","F",4000),
    ("Jen","Mary","Brown","","F",-1)
  ]

schema = StructType([ \
    StructField("firstname",StringType(),True,'val1'), \
    StructField("middlename",StringType(),True,'val2'), \
    StructField("lastname",StringType(),True,'val3'), \
    StructField("id", StringType(), True,'val4'), \
    StructField("gender", StringType(), True,'val5'), \
    StructField("salary", IntegerType(), True,'val6') \
  ])


# schema= StructType().add("firstname",StringType(),True,'val1').add("middlename",StringType(),True,'val2') \
.add("lastname",StringType(),True,'val3').add("id", StringType(), True,'val4').add("gender", StringType(), True,'val5').add("salary", IntegerType(), True,'val6')
          
         
df = spark.createDataFrame(data=data,schema=schema)
df.printSchema()
df.show(truncate=False)