code: def dataset(request):
      X, y = make_multilabel_classification(
          n_samples=int(request.param["n_samples"] * 1.2),
          n_features=request.param["n_features"],
          n_classes=request.param["n_classes"],
          n_labels=request.param["n_classes"],
          length=request.param["n_targets"],
      )
      new_x = []
      new_y = []
      for i in range(y.shape[0]):
          a = np.argwhere(y[i] == 1)[:, 0]
          if len(a) >= request.param["n_targets"]:
              new_x.append(i)
              np.random.shuffle(a)
              a = a[: request.param["n_targets"]]
              new_y.append(a)
          if len(new_x) >= request.param["n_samples"]:
              break
      X = X[new_x]
      noise = np.random.normal(0, 5.0, X.shape)
      X += noise
      y = np.array(new_y)
  
      return train_test_split(X, y, test_size=0.3)
  
file_path: cuml/python/cuml/tests/dask/test_dask_kneighbors_classifier.py
imports: from cuml.internals.safe_imports import gpu_only_import
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from cuml.neighbors import KNeighborsClassifier
  from cuml.dask.neighbors import KNeighborsClassifier
  from sklearn.datasets import make_multilabel_classification
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  import dask.array
  import dask.dataframe
  from cuml.dask.common.dask_arr_utils import to_dask_cudf
  from cuml.internals.safe_imports import gpu_only_import_from
label_desc: Use image node_id 2 for calling a global function with example usage: dataset(request) and returns: train_test_split