code: def svd_flip(u, v, u_based_decision=True):
      """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 : ndarray
          Parameters u and v are the output of `linalg.svd` or
          :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
          dimensions so one can compute `np.dot(u * s, v)`.
  
      v : ndarray
          Parameters u and v are the output of `linalg.svd` or
          :func:`~sklearn.utils.extmath.randomized_svd`, with matching inner
          dimensions so one can compute `np.dot(u * s, v)`.
          The input v should really be called vt to be consistent with scipy's
          output.
  
      u_based_decision : bool, default=True
          If True, use the columns of u as the basis for sign flipping.
          Otherwise, use the rows of v. The choice of which variable to base the
          decision on is generally algorithm dependent.
  
      Returns
      -------
      u_adjusted : ndarray
          Array u with adjusted columns and the same dimensions as u.
  
      v_adjusted : ndarray
          Array v with adjusted rows and the same dimensions as v.
      """
      if u_based_decision:
          # columns of u, rows of v
          max_abs_cols = np.argmax(np.abs(u), axis=0)
          signs = np.sign(u[max_abs_cols, range(u.shape[1])])
          u *= signs
          v *= signs[:, np.newaxis]
      else:
          # rows of v, columns of u
          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 warnings
  import numpy
  from scipy import linalg, sparse
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, get_namespace
  from ._logistic_sigmoid import _log_logistic_sigmoid
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 11 for calling a global function with example usage: svd_flip(u, v, u_based_decision) and returns: u,  v