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,
  ):
      """Generate a random n-class classification problem.
  
      This initially creates clusters of points normally distributed (std=1)
      about vertices of a `2 * class_sep`-sided hypercube, 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.
  
      Prior to shuffling, `X` stacks a number of these primary "informative"
      features, "redundant" linear combinations of these, "repeated" duplicates
      of sampled features, and arbitrary noise for and remaining features.
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      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 `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 : list of floats or None (default=None)
          The proportions of samples assigned to each class. If None, then
          classes are balanced. Note that if `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 are randomly exchanged.
  
      class_sep : float, optional (default=1.0)
          The factor multiplying the hypercube dimension.
  
      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, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      X : array of shape [n_samples, n_features]
          The generated samples.
  
      y : 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.
  
      References
      ----------
      .. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable
             selection benchmark", 2003.
  
      See also
      --------
      make_blobs: simplified variant
      make_multilabel_classification: unrelated generator for multilabel tasks
      """
      generator = check_random_state(random_state)
  
      # 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"
          )
      if 2**n_informative < n_classes * n_clusters_per_class:
          raise ValueError(
              "n_classes * n_clusters_per_class must"
              " be smaller or equal 2 ** n_informative"
          )
      if weights and len(weights) not in [n_classes, n_classes - 1]:
          raise ValueError(
              "Weights specified but incompatible with number "
              "of classes."
          )
  
      n_useless = n_features - n_informative - n_redundant - n_repeated
      n_clusters = n_classes * n_clusters_per_class
  
      if weights and len(weights) == (n_classes - 1):
          weights.append(1.0 - sum(weights))
  
      if weights is None:
          weights = [1.0 / n_classes] * n_classes
          weights[-1] = 1.0 - sum(weights[:-1])
  
      # Distribute samples among clusters by weight
      n_samples_per_cluster = []
      for k in range(n_clusters):
          n_samples_per_cluster.append(
              int(
                  n_samples
                  * weights[k % n_classes]
                  / n_clusters_per_class
              )
          )
      for i in range(n_samples - sum(n_samples_per_cluster)):
          n_samples_per_cluster[i % n_clusters] += 1
  
      # Initialize X and y
      X = np.zeros((n_samples, n_features))
      y = np.zeros(n_samples, dtype=np.int)
  
      # Build the polytope whose vertices become cluster centroids
      centroids = _generate_hypercube(
          n_clusters, n_informative, generator
      ).astype(float)
      centroids *= 2 * class_sep
      centroids -= class_sep
      if not hypercube:
          centroids *= generator.rand(n_clusters, 1)
          centroids *= generator.rand(1, n_informative)
  
      # Initially draw informative features from the standard normal
      X[:, :n_informative] = generator.randn(n_samples, n_informative)
  
      # Create each cluster; a variant of make_blobs
      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
  
          A = 2 * generator.rand(n_informative, n_informative) - 1
          X_k[...] = np.dot(X_k, A)  # introduce random covariance
  
          X_k += centroid  # shift the cluster to a vertex
  
      # Create redundant features
      if n_redundant > 0:
          B = 2 * generator.rand(n_informative, n_redundant) - 1
          X[:, n_informative : n_informative + n_redundant] = np.dot(
              X[:, :n_informative], B
          )
  
      # Repeat some features
      if n_repeated > 0:
          n = n_informative + n_redundant
          indices = ((n - 1) * generator.rand(n_repeated) + 0.5).astype(
              np.intp
          )
          X[:, n : n + n_repeated] = X[:, indices]
  
      # Fill useless features
      if n_useless > 0:
          X[:, -n_useless:] = generator.randn(n_samples, n_useless)
  
      # Randomly replace labels
      if flip_y >= 0.0:
          flip_mask = generator.rand(n_samples) < flip_y
          y[flip_mask] = generator.randint(
              n_classes, size=flip_mask.sum()
          )
  
      # Randomly shift and scale
      if shift is None:
          shift = (2 * generator.rand(n_features) - 1) * class_sep
      X += shift
  
      if scale is None:
          scale = 1 + 100 * generator.rand(n_features)
      X *= scale
  
      if shuffle:
          # Randomly permute samples
          X, y = util_shuffle(X, y, random_state=generator)
  
          # Randomly permute features
          indices = np.arange(n_features)
          generator.shuffle(indices)
          X[:, :] = X[:, indices]
  
      return X, y
  
imports: import numbers
  import array
  import numpy
  from scipy import linalg
  import scipy.sparse
  from ..preprocessing import MultiLabelBinarizer
  from ..utils import check_array, check_random_state
  from ..utils import shuffle
  from ..utils.fixes import astype
  from ..utils.random import sample_without_replacement
  from ..externals import six
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) and returns: X,  y