code: def _tile_one_chunk(cls, op: "GroupBySample", in_df, weights):
      out = op.outputs[0]
  
      input_dfs = [in_df]
      if isinstance(weights, ENTITY_TYPE):
          input_dfs.append(weights)
  
      params = out.params
      chunk_op = op.copy().reset_key()
      if isinstance(weights, ENTITY_TYPE):
          chunk_op._weights = weights
      params["index"] = (0,) * out.ndim
      chunk = chunk_op.new_chunk(
          [c.chunks[0] for c in input_dfs], **params
      )
  
      df_op = op.copy().reset_key()
      return df_op.new_tileables(
          input_dfs,
          chunks=[chunk],
          nsplits=((s,) for s in out.shape),
          **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 GroupBySample obj's underlying member method code with example usage: obj._tile_one_chunk(cls, op, in_df, weights) and returns: df_op