code: def svd_flip(u, v, u_based_decision=False):
      """Sign correction to ensure deterministic output from SVD.
  
      This function is useful for orienting eigenvectors such that
      they all lie in a shared but arbitrary half-space. This makes
      it possible to ensure that results are equivalent across SVD
      implementations and random number generator states.
  
      Parameters
      ----------
  
      u : (M, K) array_like
          Left singular vectors (in columns)
      v : (K, N) array_like
          Right singular vectors (in rows)
      u_based_decision: bool
          Whether or not to choose signs based
          on `u` rather than `v`, by default False
  
      Returns
      -------
  
      u : (M, K) array_like
          Left singular vectors with corrected sign
      v:  (K, N) array_like
          Right singular vectors with corrected sign
      """
      # Determine half-space in which all singular vectors
      # lie relative to an arbitrary vector; summation
      # equivalent to dot product with row vector of ones
      if u_based_decision:
          dtype = u.dtype
          signs = np.sum(u, axis=0, keepdims=True)
      else:
          dtype = v.dtype
          signs = np.sum(v, axis=1, keepdims=True).T
      signs = 2.0 * ((signs >= 0) - 0.5).astype(dtype)
      # Force all singular vectors into same half-space
      u, v = u * signs, v * signs.T
      return u, v
  
imports: from __future__ import annotations
  import contextlib
  import functools
  import itertools
  import math
  import numbers
  import warnings
  import numpy
  from tlz import concat, frequencies
  from dask.array.core import Array
  from dask.array.numpy_compat import AxisError
  from dask.base import is_dask_collection, tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import has_keyword, is_arraylike, is_cupy_type, typename
label_desc: Use image node_id 20 for calling a global function with example usage: svd_flip(u, v, u_based_decision) and returns: u,  v