code: def from_avro(data, jsonFormatSchema, options={}):
      
  
      sc = SparkContext._active_spark_context
      try:
          jc = sc._jvm.org.apache.spark.sql.avro.functions.from_avro(
              _to_java_column(data), jsonFormatSchema, options)
      except TypeError as e:
          if str(e) == "'JavaPackage' object is not callable":
              _print_missing_jar("Avro", "avro", "avro", sc.version)
          raise
      return Column(jc)
docs: Converts a binary column of avro format into its corresponding catalyst value. The specified
      schema must match the read data, otherwise the behavior is undefined: it may fail or return
      arbitrary result.
  
      Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the
      application as per the deployment section of "Apache Avro Data Source Guide".
  
      :param data: the binary column.
      :param jsonFormatSchema: the avro schema in JSON string format.
      :param options: options to control how the Avro record is parsed.
  
      >>> from pyspark.sql import Row
      >>> from pyspark.sql.avro.functions import from_avro, to_avro
      >>> data = [(1, Row(name='Alice', age=2))]
      >>> df = spark.createDataFrame(data, ("key", "value"))
      >>> avroDf = df.select(to_avro(df.value).alias("avro"))
      >>> avroDf.collect()
      [Row(avro=bytearray(b'\\x00\\x00\\x04\\x00\\nAlice'))]
      >>> jsonFormatSchema = '''{"type":"record","name":"topLevelRecord","fields":
      ...     [{"name":"avro","type":[{"type":"record","name":"value","namespace":"topLevelRecord",
      ...     "fields":[{"name":"age","type":["long","null"]},
      ...     {"name":"name","type":["string","null"]}]},"null"]}]}'''
      >>> avroDf.select(from_avro(avroDf.avro, jsonFormatSchema).alias("value")).collect()
      [Row(value=Row(avro=Row(age=2, name=u'Alice')))]