code: def distribute(client, data):
      if data is not None:
          n_rows = data.shape[0]
          n_workers = len(client.scheduler_info()["workers"])
          rows_per_chunk = math.ceil(n_rows / n_workers)
          if isinstance(data, (np.ndarray, cp.ndarray)):
              dask_array = da.from_array(
                  x=data, chunks={0: rows_per_chunk, 1: -1}
              )
              dask_array = dask_array.persist()
              wait(dask_array)
              client.rebalance()
              return dask_array
          elif isinstance(data, (cudf.DataFrame, cudf.Series)):
              dask_df = df.from_pandas(data, chunksize=rows_per_chunk)
              dask_df = dask_df.persist()
              wait(dask_df)
              client.rebalance()
              return dask_df
          else:
              raise ValueError("Could not distribute data")
  
imports: import os
  import json
  import time
  import math
  import itertools
  import warnings
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import
  import pytest
  from cuml.benchmark import datagen, algorithms
  from cuml.benchmark.nvtx_benchmark import Profiler
  from dask.distributed import wait
  import dask.array
  import dask.dataframe
  from copy import copy
  from cuml.benchmark.bench_helper_funcs import pass_func, fit, predict, transform, kneighbors, fit_predict, fit_transform, fit_kneighbors
label_desc: Use image node_id 1 for calling a global function with example usage: distribute(client, data) and returns: dask_array, dask_df