code: def __call__(self, groupby):
      df = groupby
      while df.op.output_types[0] not in (
          OutputType.dataframe,
          OutputType.series,
      ):
          df = df.inputs[0]
  
      selection = groupby.op.groupby_params.pop("selection", None)
      if df.ndim > 1 and selection:
          if (
              isinstance(selection, tuple)
              and selection not in df.dtypes
          ):
              selection = list(selection)
          result_df = df[selection]
      else:
          result_df = df
  
      params = result_df.params
      params["shape"] = (
          (np.nan,)
          if result_df.ndim == 1
          else (np.nan, result_df.shape[-1])
      )
      params["index_value"] = parse_index(
          result_df.index_value.to_pandas()[:0]
      )
  
      input_dfs = [df]
      if isinstance(self.weights, ENTITY_TYPE):
          input_dfs.append(self.weights)
  
      self._output_types = get_output_types(result_df)
      return self.new_tileable(input_dfs, **params)
  
imports: import copy
  import itertools
  import random
  from collections.abc import Iterable
  from typing import Optional, Sequence, Union
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, OutputType, get_output_types, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...serialization.serializables import BoolField, DictField, Float32Field, KeyField, Int32Field, Int64Field, NDArrayField, StringField
  from ...tensor.operands import TensorShuffleProxy
  from ...tensor.random import RandomStateField
  from ...tensor.utils import gen_random_seeds
  from ...utils import has_unknown_shape
  from ..initializer import Series
  from ..operands import DataFrameOperandMixin, DataFrameOperand
  from ..utils import parse_index
label_desc: Use image node_id 4 for calling the GroupBySample obj's underlying member method code with example usage: obj.__call__(groupby) and returns: self