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",
      _centroids=None,
      _informative_covariance=None,
      _redundant_covariance=None,
      _repeated_indices=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 cuml.datasets.classification import make_classification
  
          >>> X, y = make_classification(n_samples=10, n_features=4,
          ...                            n_informative=2, n_classes=2,
          ...                            random_state=10)
  
          >>> print(X) # doctest: +SKIP
          [[-1.7974224   0.24425316  0.39062843 -0.38293394]
          [ 0.6358963   1.4161923   0.06970507 -0.16085647]
          [-0.22802866 -1.1827322   0.3525861   0.276615  ]
          [ 1.7308872   0.43080002  0.05048406  0.29837844]
          [-1.9465544   0.5704457  -0.8997551  -0.27898186]
          [ 1.0575483  -0.9171263   0.09529338  0.01173469]
          [ 0.7917619  -1.0638094  -0.17599393 -0.06420116]
          [-0.6686142  -0.13951421 -0.6074711   0.21645583]
          [-0.88968956 -0.914443    0.1302423   0.02924336]
          [-0.8817671  -0.84549576  0.1845096   0.02556021]]
  
          >>> print(y)
          [0 1 0 1 1 0 0 1 0 0]
  
      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 (n_classes,) or (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
      _centroids: array of centroids of shape (n_clusters, n_informative)
      _informative_covariance: array for covariance between informative features
          of shape (n_clusters, n_informative, n_informative)
      _redundant_covariance: array for covariance between redundant features
          of shape (n_informative, n_redundant)
      _repeated_indices: array of indices for the repeated features
          of shape (n_repeated, )
  
      Returns
      -------
      X : device array of shape [n_samples, n_features]
          The generated samples.
      y : device array of shape [n_samples]
          The integer labels for class membership of each sample.
  
      Notes
      -----
      The algorithm is adapted from Guyon [1]_ and was designed to generate
      the "Madelon" dataset. How we optimized for GPUs:
  
          1. Firstly, we generate X from a standard univariate instead of zeros.
             This saves memory as we don't need to generate univariates each
             time for each feature class (informative, repeated, etc.) while
             also providing the added speedup of generating a big matrix
             on GPU
          2. We generate :py:`order=F` construction. We exploit the
             fact that X is a generated from a univariate normal, and
             covariance is introduced with matrix multiplications. Which means,
             we can generate X as a 1D array and just reshape it to the
             desired order, which only updates the metadata and eliminates
             copies
          3. Lastly, we also shuffle by construction. Centroid indices are
             permuted for each sample, and then we construct the data for
             each centroid. This shuffle works for both :py:`order=C` and
             :py:`order=F` and eliminates any need for secondary copies
  
      References
      ----------
      .. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable
             selection benchmark", 2003.
  
      """
      cuml.internals.set_api_output_type("cupy")
  
      generator = _create_rs_generator(random_state)
      np_seed = int(generator.randint(n_samples, size=1))
      np.random.seed(np_seed)
  
      # Count features, clusters and samples
      if n_informative + n_redundant + n_repeated > n_features:
          raise ValueError(
              "Number of informative, redundant and repeated "
              "features must sum to less than the number of total"
              " features"
          )
      # Use log2 to avoid overflow errors
      if n_informative < np.log2(n_classes * n_clusters_per_class):
          msg = "n_classes({}) * n_clusters_per_class({}) must be"
          msg += " smaller or equal 2**n_informative({})={}"
          raise ValueError(
              msg.format(
                  n_classes,
                  n_clusters_per_class,
                  n_informative,
                  2**n_informative,
              )
          )
  
      if weights is not None:
          if len(weights) not in [n_classes, n_classes - 1]:
              raise ValueError(
                  "Weights specified but incompatible with number "
                  "of classes."
              )
          if len(weights) == n_classes - 1:
              if isinstance(weights, list):
                  weights = weights + [1.0 - sum(weights)]
              else:
                  weights = np.resize(weights, n_classes)
                  weights[-1] = 1.0 - sum(weights[:-1])
      else:
          weights = [1.0 / n_classes] * n_classes
  
      n_clusters = n_classes * n_clusters_per_class
  
      # Distribute samples among clusters by weight
      n_samples_per_cluster = [
          int(n_samples * weights[k % n_classes] / n_clusters_per_class)
          for k in range(n_clusters)
      ]
  
      for i in range(n_samples - sum(n_samples_per_cluster)):
          n_samples_per_cluster[i % n_clusters] += 1
  
      # Initialize X and y
      X = generator.randn(n_samples * n_features, dtype=dtype)
      X = X.reshape((n_samples, n_features), order=order)
      y = cp.zeros(n_samples, dtype=np.int64)
  
      # Build the polytope whose vertices become cluster centroids
      if _centroids is None:
          centroids = cp.array(
              _generate_hypercube(n_clusters, n_informative, generator)
          ).astype(dtype, copy=False)
      else:
          centroids = _centroids
      centroids *= 2 * class_sep
      centroids -= class_sep
      if not hypercube:
          centroids *= generator.rand(n_clusters, 1, dtype=dtype)
          centroids *= generator.rand(1, n_informative, dtype=dtype)
  
      # Create redundant features
      if n_redundant > 0:
          if _redundant_covariance is None:
              B = (
                  2
                  * generator.rand(
                      n_informative, n_redundant, dtype=dtype
                  )
                  - 1
              )
          else:
              B = _redundant_covariance
  
      # Create each cluster; a variant of make_blobs
      if shuffle:
          proba_samples_per_cluster = np.array(
              n_samples_per_cluster
          ) / np.sum(n_samples_per_cluster)
          shuffled_sample_indices = cp.array(
              np.random.choice(
                  n_clusters,
                  n_samples,
                  replace=True,
                  p=proba_samples_per_cluster,
              )
          )
          for k, centroid in enumerate(centroids):
              centroid_indices = cp.where(shuffled_sample_indices == k)
              y[centroid_indices[0]] = k % n_classes
  
              X_k = X[centroid_indices[0], :n_informative]
  
              if _informative_covariance is None:
                  A = (
                      2
                      * generator.rand(
                          n_informative, n_informative, dtype=dtype
                      )
                      - 1
                  )
              else:
                  A = _informative_covariance[k]
              X_k = cp.dot(X_k, A)
  
              # NOTE: This could be done outside the loop, but a current
              # cupy bug does not allow that
              # https://github.com/cupy/cupy/issues/3284
              if n_redundant > 0:
                  X[
                      centroid_indices[0],
                      n_informative : n_informative + n_redundant,
                  ] = cp.dot(X_k, B)
  
              X_k += centroid  # shift the cluster to a vertex
              X[centroid_indices[0], :n_informative] = X_k
      else:
          stop = 0
          for k, centroid in enumerate(centroids):
              start, stop = stop, stop + n_samples_per_cluster[k]
              y[start:stop] = k % n_classes  # assign labels
              X_k = X[
                  start:stop, :n_informative
              ]  # slice a view of the cluster
  
              if _informative_covariance is None:
                  A = (
                      2
                      * generator.rand(
                          n_informative, n_informative, dtype=dtype
                      )
                      - 1
                  )
              else:
                  A = _informative_covariance[k]
              X_k = cp.dot(X_k, A)  # introduce random covariance
  
              if n_redundant > 0:
                  X[
                      start:stop,
                      n_informative : n_informative + n_redundant,
                  ] = cp.dot(X_k, B)
  
              X_k += centroid  # shift the cluster to a vertex
              X[start:stop, :n_informative] = X_k
  
      # Repeat some features
      if n_repeated > 0:
          n = n_informative + n_redundant
          if _repeated_indices is None:
              indices = (
                  (n - 1) * generator.rand(n_repeated, dtype=dtype)
                  + 0.5
              ).astype(np.intp)
          else:
              indices = _repeated_indices
          X[:, n : n + n_repeated] = X[:, indices]
  
      # Randomly replace labels
      if flip_y >= 0.0:
          flip_mask = generator.rand(n_samples, dtype=dtype) < flip_y
          y[flip_mask] = generator.randint(
              n_classes, size=int(flip_mask.sum())
          )
  
      # Randomly shift and scale
      if shift is None:
          shift = (
              2 * generator.rand(n_features, dtype=dtype) - 1
          ) * class_sep
      X += shift
  
      if scale is None:
          scale = 1 + 100 * generator.rand(n_features, dtype=dtype)
      X *= scale
  
      return X, y
  
imports: import cuml.internals
  from cuml.internals.import_utils import has_sklearn
  from cuml.datasets.utils import _create_rs_generator
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import_from, null_decorator
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, _centroids, _informative_covariance, _redundant_covariance, _repeated_indices) and returns: X,  y