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.
      This function is borrowed from scikit-learn/utils/extmath.py
      Parameters
      ----------
      U : ndarray
          u and v are the output of SVD
      V : ndarray
          u and v are the output of SVD
      u_based_decision : boolean, (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, v_adjusted : arrays with the same dimensions as the input.
      """
      if u_based_decision:
          # columns of U, rows of V
          max_abs_cols = tl.argmax(tl.abs(U), axis=0)
          signs = tl.sign(
              tl.tensor(
                  [
                      U[i, j]
                      for (i, j) in zip(
                          max_abs_cols, range(tl.shape(U)[1])
                      )
                  ],
                  **tl.context(U),
              )
          )
          U = U * signs
          if tl.shape(V)[0] > tl.shape(U)[1]:
              signs = tl.concatenate(
                  (
                      signs,
                      tl.ones(
                          tl.shape(V)[0] - tl.shape(U)[1],
                          **tl.context(V),
                      ),
                  )
              )
          V = V * signs[: tl.shape(V)[0]][:, None]
      else:
          # rows of V, columns of U
          max_abs_rows = tl.argmax(tl.abs(V), axis=1)
          signs = tl.sign(
              tl.tensor(
                  [
                      V[i, j]
                      for (i, j) in zip(
                          range(tl.shape(V)[0]), max_abs_rows
                      )
                  ],
                  **tl.context(V),
              )
          )
          V = V * signs[:, None]
          if tl.shape(U)[1] > tl.shape(V)[0]:
              signs = tl.concatenate(
                  (
                      signs,
                      tl.ones(
                          tl.shape(U)[1] - tl.shape(V)[0],
                          **tl.context(V),
                      ),
                  )
              )
          U = U * signs[: tl.shape(U)[1]]
  
      return U, V
  
file_path: tensorly/tensorly/tenalg/svd.py
imports: import warnings
  import tensorly
  from .proximal import soft_thresholding
label_desc: Use image node_id 1 for calling a global function with example usage: svd_flip(U, V, u_based_decision) and returns: U,  V