code: def tile(cls, op: "GroupBySample"):
      in_df = op.inputs[0]
      if in_df.ndim == 2:
          in_df = yield from recursive_tile(
              in_df.rechunk({1: (in_df.shape[1],)})
          )
  
      weights = op.weights
      if isinstance(weights, ENTITY_TYPE):
          weights = yield from recursive_tile(
              weights.rechunk({0: in_df.nsplits[0]})
          )
  
      if len(in_df.chunks) == 1:
          return cls._tile_one_chunk(op, in_df, weights)
      return (yield from cls._tile_distributed(op, in_df, weights))
  
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 7 for calling the GroupBySample obj's underlying member method code with example usage: obj.tile(cls, op) and returns: unknown, cls