code: def make_hastie_10_2(n_samples=12000, *, random_state=None):
      """Generate data for binary classification used in Hastie et al. 2009, Example 10.2.
  
      The ten features are standard independent Gaussian and
      the target ``y`` is defined by::
  
        y[i] = 1 if np.sum(X[i] ** 2) > 9.34 else -1
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      Parameters
      ----------
      n_samples : int, default=12000
          The number of samples.
  
      random_state : int, RandomState instance or None, default=None
          Determines random number generation for dataset creation. Pass an int
          for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      Returns
      -------
      X : ndarray of shape (n_samples, 10)
          The input samples.
  
      y : ndarray of shape (n_samples,)
          The output values.
  
      See Also
      --------
      make_gaussian_quantiles : A generalization of this dataset approach.
  
      References
      ----------
      .. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
             Learning Ed. 2", Springer, 2009.
      """
      rs = check_random_state(random_state)
  
      shape = (n_samples, 10)
      X = rs.normal(size=shape).reshape(shape)
      y = ((X**2.0).sum(axis=1) > 9.34).astype(np.float64, copy=False)
      y[y == 0.0] = -1.0
  
      return X, y
  
imports: import array
  import numbers
  import warnings
  from collections.abc import Iterable
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from scipy import linalg
  from ..preprocessing import MultiLabelBinarizer
  from ..utils import check_array, check_random_state
  from ..utils import shuffle
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.random import sample_without_replacement
label_desc: Use image node_id 4 for calling a global function with example usage: make_hastie_10_2(n_samples) and returns: X,  y