docstr: Return 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
      mode that should receive negative signs
  
  func
      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)
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code: def cp_flip_sign(cp_tensor, mode=0, func=None):
      """Return 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
          mode that should receive negative signs
  
      func
          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)
      """
      ivy.CPTensor.validate_cp_tensor(cp_tensor)
      weights, factors = cp_tensor
  
      if func is None:
          func = ivy.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 = ivy.sign(func(factors[jj], axis=0))
  
          # Update both the current and receiving factor
          factors[mode] = factors[mode] * column_signs[ivy.newaxis, :]
          factors[jj] = factors[jj] * column_signs[ivy.newaxis, :]
  
      # Check the weight signs
      weight_signs = ivy.sign(weights)
      factors[mode] = factors[mode] * weight_signs[ivy.newaxis, :]
      weights = ivy.abs(weights)
  
      return CPTensor((weights, factors))
  
imports: from .base import FactorizedTensor
  import ivy
label_desc: Use image node_id 19 for calling the CPTensor obj's underlying member method code with example usage: obj.cp_flip_sign(cp_tensor, mode, func) and returns: CPTensor