code: def shrunk_covariance(emp_cov, shrinkage=0.1):
      """Calculate a covariance matrix shrunk on the diagonal.
  
      Read more in the :ref:`User Guide <shrunk_covariance>`.
  
      Parameters
      ----------
      emp_cov : array-like of shape (n_features, n_features)
          Covariance matrix to be shrunk.
  
      shrinkage : float, default=0.1
          Coefficient in the convex combination used for the computation
          of the shrunk estimate. Range is [0, 1].
  
      Returns
      -------
      shrunk_cov : ndarray of shape (n_features, n_features)
          Shrunk covariance.
  
      Notes
      -----
      The regularized (shrunk) covariance is given by::
  
          (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
  
      where `mu = trace(cov) / n_features`.
      """
      emp_cov = check_array(emp_cov)
      n_features = emp_cov.shape[0]
  
      mu = np.trace(emp_cov) / n_features
      shrunk_cov = (1.0 - shrinkage) * emp_cov
      shrunk_cov.flat[:: n_features + 1] += shrinkage * mu
  
      return shrunk_cov
  
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 3 for calling a global function with example usage: shrunk_covariance(emp_cov, shrinkage) and returns: shrunk_cov