code: def __repr__(self) -> str:
      # the expressions are not resolved here,
      # so the string representation can be different from vanilla PySpark.
      grouping_str = ", ".join(
          str(e._expr) for e in self._grouping_cols
      )
      grouping_str = f"grouping expressions: [{grouping_str}]"
  
      value_str = ", ".join("%s: %s" % c for c in self._df.dtypes)
  
      if self._group_type == "groupby":
          type_str = "GroupBy"
      elif self._group_type == "rollup":
          type_str = "RollUp"
      elif self._group_type == "cube":
          type_str = "Cube"
      elif self._group_type == "grouping_sets":
          type_str = "GroupingSets"
      else:
          type_str = "Pivot"
  
      return f"GroupedData[{grouping_str}, value: [{value_str}], type: {type_str}]"
  
imports: from pyspark.sql.connect.utils import check_dependencies
  import warnings
  from typing import Any, Dict, List, Sequence, Union, TYPE_CHECKING, Optional, overload, cast
  from pyspark.rdd import PythonEvalType
  from pyspark.sql.group import GroupedData
  from pyspark.sql.pandas.group_ops import PandasCogroupedOps
  from pyspark.sql.types import NumericType
  from pyspark.sql.types import StructType
  import pyspark.sql.connect.plan
  from pyspark.sql.connect.column import Column
  from pyspark.sql.connect.functions.builtin import _invoke_function, col, lit
  from pyspark.errors import PySparkNotImplementedError, PySparkTypeError
label_desc: Use image node_id 2 for calling the GroupedData obj's underlying member method code with example usage: obj.__repr__() and returns: str, grouping_str, str, value_str, str, type_str, str