code: def _load_dataset(dataset, n_rows):
      if dataset == "digits":
          local_X, local_y = load_digits(return_X_y=True)
  
      else:  # dataset == "iris"
          local_X, local_y = load_iris(return_X_y=True)
  
      local_X = cp.asarray(local_X)
      local_y = cp.asarray(local_y)
  
      local_X = local_X.repeat(math.ceil(n_rows / len(local_X)), axis=0)
      local_y = local_y.repeat(math.ceil(n_rows / len(local_y)), axis=0)
  
      # Add some gaussian noise
      local_X += cp.random.standard_normal(
          local_X.shape, dtype=cp.float32
      )
  
      return local_X, local_y
  
file_path: cuml/python/cuml/tests/dask/test_dask_umap.py
imports: from sklearn.datasets import load_iris
  from sklearn.datasets import load_digits
  import math
  from cuml.metrics import trustworthiness
  from cuml.internals import logger
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: _load_dataset(dataset, n_rows) and returns: local_X,  local_y