code: def pivot(
      self, pivot_col: str, values: Optional[List["LiteralType"]] = None
  ) -> "GroupedData":
      if self._group_type != "groupby":
          if self._group_type == "pivot":
              raise PySparkNotImplementedError(
                  error_class="UNSUPPORTED_OPERATION",
                  message_parameters={
                      "operation": "Repeated PIVOT operation"
                  },
              )
          else:
              raise PySparkNotImplementedError(
                  error_class="UNSUPPORTED_OPERATION",
                  message_parameters={
                      "operation": f"PIVOT after {self._group_type.upper()}"
                  },
              )
  
      if not isinstance(pivot_col, str):
          raise PySparkTypeError(
              error_class="NOT_STR",
              message_parameters={
                  "arg_name": "pivot_col",
                  "arg_type": type(pivot_col).__name__,
              },
          )
  
      if values is not None:
          if not isinstance(values, list):
              raise PySparkTypeError(
                  error_class="NOT_LIST",
                  message_parameters={
                      "arg_name": "values",
                      "arg_type": type(values).__name__,
                  },
              )
          for v in values:
              if not isinstance(v, (bool, float, int, str)):
                  raise PySparkTypeError(
                      error_class="NOT_BOOL_OR_FLOAT_OR_INT_OR_STR",
                      message_parameters={
                          "arg_name": "value",
                          "arg_type": type(v).__name__,
                      },
                  )
  
      return GroupedData(
          df=self._df,
          group_type="pivot",
          grouping_cols=self._grouping_cols,
          pivot_col=self._df[pivot_col],
          pivot_values=values,
      )
  
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 12 for calling the GroupedData obj's underlying member method code with example usage: obj.pivot(pivot_col, values) and returns: GroupedData