code: def agg(self, *exprs: Union[Column, Dict[str, str]]) -> "DataFrame":
      from pyspark.sql.connect.dataframe import DataFrame
  
      assert exprs, "exprs should not be empty"
      if len(exprs) == 1 and isinstance(exprs[0], dict):
          # Convert the dict into key value pairs
          aggregate_cols = [
              _invoke_function(exprs[0][k], col(k)) for k in exprs[0]
          ]
      else:
          # Columns
          assert all(
              isinstance(c, Column) for c in exprs
          ), "all exprs should be Column"
          aggregate_cols = cast(List[Column], list(exprs))
  
      return DataFrame(
          plan.Aggregate(
              child=self._df._plan,
              group_type=self._group_type,
              grouping_cols=self._grouping_cols,
              aggregate_cols=aggregate_cols,
              pivot_col=self._pivot_col,
              pivot_values=self._pivot_values,
              grouping_sets=self._grouping_sets,
          ),
          session=self._df._session,
      )
  
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 5 for calling the GroupedData obj's underlying member method code with example usage: obj.agg() and returns: DataFrame