code: def __call__(self, df):
      self._output_types = [OutputType.tensor]
      inp_tileables = [df]
      if self.weights is not None:
          inp_tileables.append(self.weights)
      return self.new_tileable(
          inp_tileables, dtype=np.dtype(np.int_), shape=(np.nan,)
      )
  
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 GroupBySampleILoc obj's underlying member method code with example usage: obj.__call__(df) and returns: self