code: def cov_nearest(
      cov,
      method="clipped",
      threshold=1e-15,
      n_fact=100,
      return_all=False,
  ):
      """
      Find the nearest covariance matrix that is positive (semi-) definite
  
      This leaves the diagonal, i.e. the variance, unchanged
  
      Parameters
      ----------
      cov : ndarray, (k,k)
          initial covariance matrix
      method : str
          if "clipped", then the faster but less accurate ``corr_clipped`` is
          used.if "nearest", then ``corr_nearest`` is used
      threshold : float
          clipping threshold for smallest eigen value, see Notes
      n_fact : int or float
          factor to determine the maximum number of iterations in
          ``corr_nearest``. See its doc string
      return_all : bool
          if False (default), then only the covariance matrix is returned.
          If True, then correlation matrix and standard deviation are
          additionally returned.
  
      Returns
      -------
      cov_ : ndarray
          corrected covariance matrix
      corr_ : ndarray, (optional)
          corrected correlation matrix
      std_ : ndarray, (optional)
          standard deviation
  
  
      Notes
      -----
      This converts the covariance matrix to a correlation matrix. Then, finds
      the nearest correlation matrix that is positive semidefinite and converts
      it back to a covariance matrix using the initial standard deviation.
  
      The smallest eigenvalue of the intermediate correlation matrix is
      approximately equal to the ``threshold``.
      If the threshold=0, then the smallest eigenvalue of the correlation matrix
      might be negative, but zero within a numerical error, for example in the
      range of -1e-16.
  
      Assumes input covariance matrix is symmetric.
  
      See Also
      --------
      corr_nearest
      corr_clipped
      """
  
      from statsmodels.stats.moment_helpers import cov2corr, corr2cov
  
      cov_, std_ = cov2corr(cov, return_std=True)
      if method == "clipped":
          corr_ = corr_clipped(cov_, threshold=threshold)
      else:  # method == 'nearest'
          corr_ = corr_nearest(cov_, threshold=threshold, n_fact=n_fact)
  
      cov_ = corr2cov(corr_, std_)
  
      if return_all:
          return cov_, corr_, std_
      else:
          return cov_
  
imports: import numpy
  import scipy.sparse
  from scipy.sparse.linalg import svds
  from scipy.optimize import fminbound
  import warnings
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import IterationLimitWarning, iteration_limit_doc
label_desc: Use image node_id 4 for calling a global function with example usage: cov_nearest(cov, method, threshold, n_fact, return_all) and returns: cov_,  corr_,  std_, cov_