code: def tile(cls, op: "GroupBySampleILoc"):
      in_df = op.inputs[0]
      out_tensor = op.outputs[0]
      iloc_col_header = _ILOC_COL_HEADER + str(op.random_col_id)
      weight_col_header = _WEIGHT_COL_HEADER + str(op.random_col_id)
  
      if has_unknown_shape(in_df):
          yield
  
      if op.weights is None:
          weights_iter = itertools.repeat(None)
      else:
          weights_iter = iter(op.weights.chunks)
  
      if isinstance(op.groupby_params["by"], list):
          map_cols = list(op.groupby_params["by"])
      else:  # pragma: no cover
          map_cols = []
  
      dtypes = in_df.dtypes.copy()
      dtypes.at[iloc_col_header] = np.dtype(np.int_)
      map_cols.append(iloc_col_header)
      if op.weights is not None:
          dtypes.at[weight_col_header] = op.weights.dtype
          map_cols.append(weight_col_header)
  
      new_dtypes = dtypes[map_cols]
      new_columns_value = parse_index(new_dtypes.index, store_data=True)
  
      map_chunks = []
      left_ilocs = np.array((0,) + in_df.nsplits[0]).cumsum()
      for inp_chunk, weight_chunk in zip(in_df.chunks, weights_iter):
          new_op = op.copy().reset_key()
          new_op.left_iloc_bound = int(left_ilocs[inp_chunk.index[0]])
          new_op.stage = OperandStage.map
          new_op.output_types = [OutputType.dataframe]
  
          inp_chunks = [inp_chunk]
          if weight_chunk is not None:
              inp_chunks.append(weight_chunk)
          params = inp_chunk.params
          params.update(
              dict(
                  dtypes=new_dtypes,
                  columns_value=new_columns_value,
                  shape=(inp_chunk.shape[0], len(new_dtypes)),
                  index=inp_chunk.index,
              )
          )
          map_chunks.append(new_op.new_chunk(inp_chunks, **params))
  
      new_op = op.copy().reset_key()
      new_op._output_types = [OutputType.dataframe]
      params = in_df.params
      params.update(
          dict(
              chunks=map_chunks,
              nsplits=(in_df.nsplits[0], (len(new_dtypes),)),
              dtypes=new_dtypes,
              columns_value=new_columns_value,
              shape=(in_df.shape[0], len(new_dtypes)),
          )
      )
      map_df = new_op.new_tileable(op.inputs, **params)
  
      groupby_params = op.groupby_params.copy()
      groupby_params.pop("selection", None)
      grouped = yield from recursive_tile(
          map_df.groupby(**groupby_params)
      )
  
      result_chunks = []
      seeds = gen_random_seeds(len(grouped.chunks), op.random_state)
      for group_chunk, seed in zip(grouped.chunks, seeds):
          new_op = op.copy().reset_key()
          new_op.stage = OperandStage.reduce
          new_op.weights = None
          new_op._random_state = None
          new_op.seed = seed
  
          result_chunks.append(
              new_op.new_chunk(
                  [group_chunk],
                  shape=(np.nan,),
                  index=(group_chunk.index[0],),
                  dtype=out_tensor.dtype,
              )
          )
  
      new_op = op.copy().reset_key()
      params = out_tensor.params
      params.update(
          dict(
              chunks=result_chunks,
              nsplits=((np.nan,) * len(result_chunks),),
          )
      )
      return new_op.new_tileables(op.inputs, **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 5 for calling the GroupBySampleILoc obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op