code: def svd_flip(
      U: Union[ivy.Array, ivy.NativeArray],
      V: Union[ivy.Array, ivy.NativeArray],
      /,
      u_based_decision: Optional[bool] = True,
  ) -> Tuple[ivy.Array, ivy.Array]:
      """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
          left singular matrix output of SVD
      V
          right singular matrix output of SVD
      u_based_decision
          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 = ivy.argmax(ivy.abs(U), axis=0)
          signs = ivy.sign(
              ivy.array(
                  [
                      U[i, j]
                      for (i, j) in zip(
                          max_abs_cols, range(ivy.shape(U)[1])
                      )
                  ],
              )
          )
          U = U * signs
          if ivy.shape(V)[0] > ivy.shape(U)[1]:
              signs = ivy.concat(
                  (signs, ivy.ones(ivy.shape(V)[0] - ivy.shape(U)[1]))
              )
          V = V * signs[: ivy.shape(V)[0]][:, None]
      else:
          # rows of V, columns of U
          max_abs_rows = ivy.argmax(ivy.abs(V), axis=1)
          signs = ivy.sign(
              ivy.array(
                  [
                      V[i, j]
                      for (i, j) in zip(
                          range(ivy.shape(V)[0]), max_abs_rows
                      )
                  ],
              )
          )
          V = V * signs[:, None]
          if ivy.shape(U)[1] > ivy.shape(V)[0]:
              signs = ivy.concat(
                  (
                      signs,
                      ivy.ones(
                          ivy.shape(U)[1] - ivy.shape(V)[0],
                      ),
                  )
              )
          U = U * signs[: ivy.shape(U)[1]]
  
      return U, V
  
imports: import logging
  from typing import Union, Optional, Tuple, List, Sequence, Literal
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import to_native_arrays_and_back, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_array_function, handle_device, inputs_to_ivy_arrays, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 17 for calling a global function with example usage: svd_flip(u_based_decision) and returns: U,  V