code: def _data_from_multivariate_normal(
      client, rs, covar, chunksizes, n_features, dtype
  ):
      workers = list(client.has_what().keys())
  
      n_chunks = len(chunksizes)
      chunks_workers = (workers * n_chunks)[:n_chunks]
  
      chunk_seeds = rs.permutation(len(chunksizes))
  
      data_parts = [
          client.submit(
              _dask_data_from_multivariate_normal,
              chunk_seeds[idx],
              covar,
              chunksizes[idx],
              n_features,
              dtype,
              workers=[chunks_workers[idx]],
              pure=False,
          )
          for idx, chunk in enumerate(chunksizes)
      ]
  
      data_dela = _create_delayed(
          data_parts, dtype, chunksizes, n_features
      )
  
      return da.concatenate(data_dela, axis=0)
  
imports: from cuml.dask.common.utils import get_client
  from cuml.dask.common.input_utils import DistributedDataHandler
  from cuml.common import with_cupy_rmm
  from cuml.dask.datasets.utils import _create_delayed
  from cuml.dask.datasets.utils import _get_labels
  from cuml.dask.datasets.utils import _get_X
  from cuml.internals.safe_imports import gpu_only_import
  import dask.array
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 5 for calling a global function with example usage: _data_from_multivariate_normal(client, rs, covar, chunksizes, n_features, dtype) and returns: da