class_docstr: InputLoader that reads data in as a Spark DataFrame.
class_docstr_tok: ["InputLoader","that","reads","data","in","as","a","Spark","DataFrame","."]
docstr: Parse a Feature Group's name from an arn.
  
  Args:
      fg_uri (str): a string identifier of the Feature Group.
  
  Returns:
      str: the name of the feature group.
docstr_tok: ["Parse","a","Feature","Group","'s","name","from","an","arn",".","Args",":","fg_uri","(","str",")",":","a","string","identifier","of","the","Feature","Group",".","Returns",":","str",":","the","name","of","the","feature","group","."]
code: def _parse_name_from_arn(self, fg_uri: str) -> str:
      """Parse a Feature Group's name from an arn.
  
      Args:
          fg_uri (str): a string identifier of the Feature Group.
  
      Returns:
          str: the name of the feature group.
      """
      match = re.match(FEATURE_GROUP_ARN_REGEX_PATTERN, fg_uri)
      if match:
          feature_group_name = match.group(4)
          return feature_group_name
      return fg_uri
  
imports: from __future__ import absolute_import
  import logging
  import re
  from abc import ABC, abstractmethod
  from typing import Generic, Optional, TypeVar, Union
  import attr
  from pyspark.sql import DataFrame
  from sagemaker import Session
  from sagemaker.feature_store.feature_processor._constants import FEATURE_GROUP_ARN_REGEX_PATTERN
  from sagemaker.feature_store.feature_processor._data_source import CSVDataSource, FeatureGroupDataSource, ParquetDataSource, IcebergTableDataSource
  from sagemaker.feature_store.feature_processor._spark_factory import SparkSessionFactory
  from sagemaker.feature_store.feature_processor._input_offset_parser import InputOffsetParser
  from sagemaker.feature_store.feature_processor._env import EnvironmentHelper
label_desc: Use image node_id 7 for calling the SparkDataFrameInputLoader obj's underlying member method code with example usage: obj._parse_name_from_arn(fg_uri) and returns: fg_uri, feature_group_name