code: def svd_flip(u, v):
      """
      Sign correction to ensure deterministic output from SVD.
      Adjusts the columns of u and the rows of v such that the
      loadings in the columns in u that are largest in absolute
      value are always positive.
  
      Parameters
      ----------
      u, v : ndarray
          u and v are the output of `linalg.svd` or
          `sklearn.utils.extmath.randomized_svd`, with matching inner
          dimensions so one can compute `np.dot(u * s, v)`.
  
      Notes
      -----
      scikit-learn implementation
  
      Returns
      -------
      u_adjusted, v_adjusted : arrays with the same dimensions
      as the input.
      """
      max_abs_rows = np.argmax(np.abs(v), axis=1)
      signs = np.sign(v[range(v.shape[0]), max_abs_rows])
  
      u *= signs
      v *= signs[:, np.newaxis]
  
      return u, v
  
imports: import numpy
  from scipy import linalg
label_desc: Use image node_id 1 for calling a global function with example usage: svd_flip(u, v) and returns: u,  v