code: def cp_flip_sign(cp_tensor, mode=0, func=None):
      """Returns cp_tensor with factors flipped to have positive signs.
      The sign of a given column is determined by `func`, which is the mean
      by default. Any negative signs are assigned to the mode indicated by `mode`.
  
      Parameters
      ----------
      cp_tensor : CPTensor = (weight, factors)
          factors is list of matrices, all with the same number of columns
          i.e.::
  
              for u in U:
                  u[i].shape == (s_i, R)
  
          where `R` is fixed while `s_i` can vary with `i`
  
      mode: int
          mode that should receive negative signs
  
      func: tensorly function
          a function that should summarize the sign of a column
          it must be able to take an axis argument
  
      Returns
      -------
      CPTensor = (normalisation_weights, normalised_factors)
      """
      _validate_cp_tensor(cp_tensor)
      weights, factors = cp_tensor
  
      if func is None:
          func = T.mean
  
      for jj in range(0, len(factors)):
          # Skip the target mode
          if jj == mode:
              continue
  
          # Calculate the sign of the current factor in each component
          column_signs = T.sign(func(factors[jj], axis=0))
  
          # Update both the current and receiving factor
          factors[mode] = factors[mode] * column_signs[np.newaxis, :]
          factors[jj] = factors[jj] * column_signs[np.newaxis, :]
  
      # Check the weight signs
      weight_signs = T.sign(weights)
      factors[mode] = factors[mode] * weight_signs[np.newaxis, :]
      weights = T.abs(weights)
  
      return CPTensor((weights, factors))
  
imports: from .None import backend
  from .base import fold, tensor_to_vec
  from ._factorized_tensor import FactorizedTensor
  from .tenalg import khatri_rao, unfolding_dot_khatri_rao
  from .metrics.factors import congruence_coefficient
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: cp_flip_sign(cp_tensor, mode, func) and returns: CPTensor