code: def make_classification(
      n_samples=100,
      n_features=20,
      n_informative=2,
      n_redundant=2,
      n_repeated=0,
      n_classes=2,
      n_clusters_per_class=2,
      weights=None,
      flip_y=0.01,
      class_sep=1.0,
      hypercube=True,
      shift=0.0,
      scale=1.0,
      shuffle=True,
      random_state=None,
      order="F",
      dtype="float32",
      n_parts=None,
      client=None,
  ):
      """
      Generate a random n-class classification problem.
  
      This initially creates clusters of points normally distributed (std=1)
      about vertices of an `n_informative`-dimensional hypercube with sides of
      length :py:`2 * class_sep` and assigns an equal number of clusters to each
      class. It introduces interdependence between these features and adds
      various types of further noise to the data.
  
      Without shuffling, `X` horizontally stacks features in the following
      order: the primary `n_informative` features, followed by `n_redundant`
      linear combinations of the informative features, followed by `n_repeated`
      duplicates, drawn randomly with replacement from the informative and
      redundant features. The remaining features are filled with random noise.
      Thus, without shuffling, all useful features are contained in the columns
      :py:`X[:, :n_informative + n_redundant + n_repeated]`.
  
      Examples
      --------
      .. code-block:: python
  
          >>> from dask.distributed import Client
          >>> from dask_cuda import LocalCUDACluster
          >>> from cuml.dask.datasets.classification import make_classification
          >>> cluster = LocalCUDACluster()
          >>> client = Client(cluster)
          >>> X, y = make_classification(n_samples=10, n_features=4,
          ...                            random_state=1, n_informative=2,
          ...                            n_classes=2)
          >>> print(X.compute()) # doctest: +SKIP
          [[-1.1273878   1.2844919  -0.32349187  0.1595734 ]
          [ 0.80521786 -0.65946865 -0.40753683  0.15538901]
          [ 1.0404129  -1.481386    1.4241115   1.2664981 ]
          [-0.92821544 -0.6805706  -0.26001272  0.36004275]
          [-1.0392245  -1.1977317   0.16345565 -0.21848428]
          [ 1.2273135  -0.529214    2.4799604   0.44108105]
          [-1.9163864  -0.39505136 -1.9588828  -1.8881643 ]
          [-0.9788184  -0.89851004 -0.08339313  0.1130247 ]
          [-1.0549078  -0.8993015  -0.11921967  0.04821599]
          [-1.8388828  -1.4063598  -0.02838472 -1.0874642 ]]
          >>> print(y.compute()) # doctest: +SKIP
          [1 0 0 0 0 1 0 0 0 0]
          >>> client.close()
          >>> cluster.close()
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The number of samples.
      n_features : int, optional (default=20)
          The total number of features. These comprise `n_informative`
          informative features, `n_redundant` redundant features,
          `n_repeated` duplicated features and
          :py:`n_features-n_informative-n_redundant-n_repeated` useless features
          drawn at random.
      n_informative : int, optional (default=2)
          The number of informative features. Each class is composed of a number
          of gaussian clusters each located around the vertices of a hypercube
          in a subspace of dimension `n_informative`. For each cluster,
          informative features are drawn independently from  N(0, 1) and then
          randomly linearly combined within each cluster in order to add
          covariance. The clusters are then placed on the vertices of the
          hypercube.
      n_redundant : int, optional (default=2)
          The number of redundant features. These features are generated as
          random linear combinations of the informative features.
      n_repeated : int, optional (default=0)
          The number of duplicated features, drawn randomly from the informative
          and the redundant features.
      n_classes : int, optional (default=2)
          The number of classes (or labels) of the classification problem.
      n_clusters_per_class : int, optional (default=2)
          The number of clusters per class.
      weights : array-like of shape :py:`(n_classes,)` or :py:`(n_classes - 1,)`\
          , (default=None)
          The proportions of samples assigned to each class. If None, then
          classes are balanced. Note that if :py:`len(weights) == n_classes - 1`,
          then the last class weight is automatically inferred.
          More than `n_samples` samples may be returned if the sum of
          `weights` exceeds 1.
      flip_y : float, optional (default=0.01)
          The fraction of samples whose class is assigned randomly. Larger
          values introduce noise in the labels and make the classification
          task harder.
      class_sep : float, optional (default=1.0)
          The factor multiplying the hypercube size.  Larger values spread
          out the clusters/classes and make the classification task easier.
      hypercube : boolean, optional (default=True)
          If True, the clusters are put on the vertices of a hypercube. If
          False, the clusters are put on the vertices of a random polytope.
      shift : float, array of shape [n_features] or None, optional (default=0.0)
          Shift features by the specified value. If None, then features
          are shifted by a random value drawn in [-class_sep, class_sep].
      scale : float, array of shape [n_features] or None, optional (default=1.0)
          Multiply features by the specified value. If None, then features
          are scaled by a random value drawn in [1, 100]. Note that scaling
          happens after shifting.
      shuffle : boolean, optional (default=True)
          Shuffle the samples and the features.
      random_state : int, RandomState instance or None (default)
          Determines random number generation for dataset creation. Pass an int
          for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
      order: str, optional (default='F')
          The order of the generated samples
      dtype : str, optional (default='float32')
          Dtype of the generated samples
      n_parts : int (default = None)
          number of partitions to generate (this can be greater
          than the number of workers)
  
      Returns
      -------
      X : dask.array backed by CuPy array of shape [n_samples, n_features]
          The generated samples.
      y : dask.array backed by CuPy array of shape [n_samples]
          The integer labels for class membership of each sample.
  
      Notes
      -----
      How we extended the dask MNMG version from the single GPU version:
  
      1. We generate centroids of shape ``(n_centroids, n_informative)``
      2. We generate an informative covariance of shape \
          ``(n_centroids, n_informative, n_informative)``
      3. We generate a redundant covariance of shape \
          ``(n_informative, n_redundant)``
      4. We generate the indices for the repeated features \
      We pass along the references to the futures of the above arrays \
      with each part to the single GPU \
      `cuml.datasets.classification.make_classification` so that each \
      part (and worker) has access to the correct values to generate \
      data from the same covariances
  
      """
  
      client = get_client(client=client)
  
      rs = _create_rs_generator(random_state)
  
      workers = list(client.scheduler_info()["workers"].keys())
  
      n_parts = n_parts if n_parts is not None else len(workers)
      parts_workers = (workers * n_parts)[:n_parts]
  
      n_clusters = n_classes * n_clusters_per_class
  
      # create centroids
      centroids = cp.array(
          _generate_hypercube(n_clusters, n_informative, rs)
      ).astype(dtype, copy=False)
  
      covariance_seeds = rs.randint(n_features, size=2)
      informative_covariance = client.submit(
          _create_covariance,
          (n_clusters, n_informative, n_informative),
          int(covariance_seeds[0]),
          pure=False,
      )
  
      redundant_covariance = client.submit(
          _create_covariance,
          (n_informative, n_redundant),
          int(covariance_seeds[1]),
          pure=False,
      )
  
      # repeated indices
      n = n_informative + n_redundant
      repeated_indices = (
          (n - 1) * rs.rand(n_repeated, dtype=dtype) + 0.5
      ).astype(np.intp)
  
      # scale and shift
      if shift is None:
          shift = (2 * rs.rand(n_features, dtype=dtype) - 1) * class_sep
  
      if scale is None:
          scale = 1 + 100 * rs.rand(n_features, dtype=dtype)
  
      # Create arrays on each worker (gpu)
      rows_per_part = max(1, int(n_samples / n_parts))
  
      worker_rows = [rows_per_part] * n_parts
  
      worker_rows[-1] += n_samples % n_parts
  
      worker_rows = tuple(worker_rows)
  
      part_seeds = rs.permutation(n_parts)
      parts = [
          client.submit(
              sg_make_classification,
              worker_rows[i],
              n_features,
              n_informative,
              n_redundant,
              n_repeated,
              n_classes,
              n_clusters_per_class,
              weights,
              flip_y,
              class_sep,
              hypercube,
              shift,
              scale,
              shuffle,
              int(part_seeds[i]),
              order,
              dtype,
              centroids,
              informative_covariance,
              redundant_covariance,
              repeated_indices,
              pure=False,
              workers=[parts_workers[i]],
          )
          for i in range(len(parts_workers))
      ]
  
      X_parts = [
          client.submit(_get_X, f, pure=False)
          for idx, f in enumerate(parts)
      ]
      y_parts = [
          client.submit(_get_labels, f, pure=False)
          for idx, f in enumerate(parts)
      ]
  
      X_dela = _create_delayed(X_parts, dtype, worker_rows, n_features)
      y_dela = _create_delayed(y_parts, np.int64, worker_rows)
  
      X = da.concatenate(X_dela)
      y = da.concatenate(y_dela)
  
      return X, y
  
file_path: cuml/python/cuml/dask/datasets/classification.py
imports: from cuml.internals.safe_imports import cpu_only_import
  from cuml.datasets.classification import _generate_hypercube
  from cuml.datasets.classification import make_classification
  from cuml.datasets.utils import _create_rs_generator
  from cuml.dask.datasets.utils import _get_X
  from cuml.dask.datasets.utils import _get_labels
  from cuml.dask.datasets.utils import _create_delayed
  from cuml.dask.common.utils import get_client
  from cuml.common import with_cupy_rmm
  import dask.array
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 2 for calling a global function with example usage: make_classification(n_samples, n_features, n_informative, n_redundant, n_repeated, n_classes, n_clusters_per_class, weights, flip_y, class_sep, hypercube, shift, scale, shuffle, random_state, order, dtype, n_parts, client) and returns: X,  y