code: def _replicate_df(
      df: cudf.DataFrame,
      replication_factor: int,
      col_item_counts: Dict[str, int],
      partition_info: Optional[Union[dict, str]] = None,
  ):
      # Required by dask; need to skip dummy partitions.
      if partition_info is None:
          return cudf.DataFrame(
              {
                  col: cudf.Series(dtype=df[col].dtype)
                  for col in col_item_counts.keys()
              }
          )
  
      original_df = df.copy()
  
      if replication_factor > 1:
          for r in range(1, replication_factor):
              df_replicated = original_df.copy()
              for col, offset in col_item_counts.items():
                  df_replicated[col] += offset * r
  
              df = cudf.concat([df, df_replicated], ignore_index=True)
  
      return df
  
imports: import logging
  import warnings
  import argparse
  import traceback
  from cugraph.testing.mg_utils import generate_edgelist_rmat, get_allocation_counts_dask_lazy, sizeof_fmt, get_peak_output_ratio_across_workers, start_dask_client, stop_dask_client, enable_spilling
  from cugraph.structure.symmetrize import symmetrize
  from cugraph.gnn import BulkSampler
  import cugraph
  import json
  import re
  import os
  import gc
  from time import sleep, perf_counter
  from math import ceil
  import pandas
  import numpy
  import cupy
  import cudf
  import dask_cudf
  import dask.dataframe
  from dask.distributed import default_client
  from cugraph.dask import get_n_workers
  from typing import Optional, Union, Dict
label_desc: Use image node_id 5 for calling a global function with example usage: _replicate_df(df, replication_factor, col_item_counts, partition_info) and returns: df, cudf