code: def oas(X, *, assume_centered=False):
      """Estimate covariance with the Oracle Approximating Shrinkage as proposed in [1]_.
  
      Read more in the :ref:`User Guide <shrunk_covariance>`.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Data from which to compute the covariance estimate.
  
      assume_centered : bool, default=False
        If True, data will not be centered before computation.
        Useful to work with data whose mean is significantly equal to
        zero but is not exactly zero.
        If False, data will be centered before computation.
  
      Returns
      -------
      shrunk_cov : array-like of shape (n_features, n_features)
          Shrunk covariance.
  
      shrinkage : float
          Coefficient in the convex combination used for the computation
          of the shrunk estimate.
  
      Notes
      -----
      The regularised covariance is:
  
      (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features),
  
      where mu = trace(cov) / n_features and shrinkage is given by the OAS formula
      (see [1]_).
  
      The shrinkage formulation implemented here differs from Eq. 23 in [1]_. In
      the original article, formula (23) states that 2/p (p being the number of
      features) is multiplied by Trace(cov*cov) in both the numerator and
      denominator, but this operation is omitted because for a large p, the value
      of 2/p is so small that it doesn't affect the value of the estimator.
  
      References
      ----------
      .. [1] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.",
             Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O.
             IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010.
             <0907.4698>`
      """
      estimator = OAS(
          assume_centered=assume_centered,
      ).fit(X)
      return estimator.covariance_, estimator.shrinkage_
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import _fit_context
  from ..utils import check_array
  from ..utils._param_validation import Interval, validate_params
  from .None import EmpiricalCovariance, empirical_covariance
label_desc: Use image node_id 6 for calling a global function with example usage: oas(X) and returns: estimator,  estimator