code: def _svd_cov(cov, data):
      """Use a covariance matrix to compute the SVD faster."""
      # This makes use of mathematical equivalences between PCA and SVD
      # on zero-mean data
      s, u = np.linalg.eigh(cov)
      norm = np.ones((s.size,))
      mask = s > np.finfo(float).eps * s[-1]  # largest is last
      s = np.sqrt(s, out=s)
      norm[mask] = 1.0 / s[mask]
      u *= norm
      v = np.dot(u.T[mask], data)
      return u, s, v
  
file_path: mne-python/mne/preprocessing/otp.py
imports: from functools import partial
  import numpy
  from .._fiff.pick import _picks_to_idx
  from .._ola import _COLA, _Storer
  from ..surface import _normalize_vectors
  from ..utils import logger, verbose
label_desc: Use image node_id 1 for calling a global function with example usage: _svd_cov(cov, data) and returns: u,  s,  v