code: def svd_flip_fast(u, v):
      # Temporary svd_flip correction that bases signs
      # on right singular vectors and avoids in-memory evaluation.
      #
      # This can eventually be replaced by
      # dask.array.utils.svd_flip(..., u_based_decision=False),
      # once it is released.
      dtype = v.dtype
      signs = np.sum(v, axis=1, keepdims=True)
      signs = np.where(signs >= 0, dtype.type(1), dtype.type(-1))
      u, v = u * signs.T, v * signs
      return u, v
  
file_path: dask-ml/dask_ml/decomposition/incremental_pca.py
imports: import dask
  import numpy
  from dask import array, compute
  from dask.array import linalg
  from scipy import sparse
  from sklearn.utils import gen_batches
  from sklearn.utils.validation import check_random_state
  from .._compat import DASK_2_26_0, DASK_2_28_0
  from .._utils import draw_seed
  from ..utils import check_array, svd_flip
  from .None import pca
  from .extmath import _incremental_mean_and_var
label_desc: Use image node_id 1 for calling a global function with example usage: svd_flip_fast(u, v) and returns: u,  v