code: def _tile_distributed(cls, op: "GroupBySample", in_df, weights):
      out_df = op.outputs[0]
      if has_unknown_shape(in_df):
          yield
  
      sample_iloc_op = GroupBySampleILoc(
          groupby_params=op.groupby_params,
          size=op.size,
          frac=op.frac,
          replace=op.replace,
          weights=weights,
          random_state=op.random_state,
          errors=op.errors,
          seed=None,
          left_iloc_bound=None,
      )
      sampled_iloc = yield from recursive_tile(sample_iloc_op(in_df))
  
      map_chunks = []
      for c in sampled_iloc.chunks:
          new_op = op.copy().reset_key()
          new_op.stage = OperandStage.map
          new_op.weights = None
          new_op.output_types = [OutputType.tensor]
          new_op.input_nsplits = np.array(in_df.nsplits[0])
  
          map_chunks.append(
              new_op.new_chunk(
                  [c],
                  dtype=sampled_iloc.dtype,
                  shape=(np.nan,),
                  index=c.index,
              )
          )
  
      proxy_chunk = TensorShuffleProxy(
          dtype=sampled_iloc.dtype
      ).new_chunk(map_chunks, shape=())
  
      reduce_chunks = []
      for ordinal, src_chunk in enumerate(in_df.chunks):
          new_op = op.copy().reset_key()
          new_op.weights = None
          new_op.output_types = [OutputType.tensor]
          new_op.stage = OperandStage.reduce
          new_op.reducer_index = (src_chunk.index[0],)
          new_op.reducer_ordinal = ordinal
          new_op.n_reducers = len(in_df.chunks)
          new_op.input_nsplits = np.array(in_df.nsplits[0])
  
          reduce_chunks.append(
              new_op.new_chunk(
                  [proxy_chunk],
                  index=src_chunk.index,
                  dtype=sampled_iloc.dtype,
                  shape=(np.nan,),
              )
          )
  
      combine_chunks = []
      for src_chunk, reduce_chunk in zip(in_df.chunks, reduce_chunks):
          new_op = op.copy().reset_key()
          new_op.stage = OperandStage.combine
          new_op._weights = None
  
          params = out_df.params
          if out_df.ndim == 2:
              params.update(
                  dict(
                      index=src_chunk.index,
                      dtypes=out_df.dtypes,
                      shape=(np.nan, out_df.shape[1]),
                      columns_value=out_df.columns_value,
                  )
              )
          else:
              params.update(
                  dict(
                      index=(src_chunk.index[0],),
                      dtype=out_df.dtype,
                      shape=(np.nan,),
                      name=out_df.name,
                  )
              )
          combine_chunks.append(
              new_op.new_chunk([src_chunk, reduce_chunk], **params)
          )
  
      new_op = op.copy().reset_key()
      if out_df.ndim == 2:
          new_nsplits = (
              (np.nan,) * in_df.chunk_shape[0],
              (out_df.shape[1],),
          )
      else:
          new_nsplits = ((np.nan,) * in_df.chunk_shape[0],)
      return new_op.new_tileables(
          out_df.inputs,
          chunks=combine_chunks,
          nsplits=new_nsplits,
          **out_df.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 6 for calling the GroupBySample obj's underlying member method code with example usage: obj._tile_distributed(cls, op, in_df, weights) and returns: new_op