code: def EquilibrateEigenVectorPhases(x, y):
      """Equilibrate the phase of the Eigenvectors in the columns of `x` and `y`.
  
      Eigenvectors are only unique up to an arbitrary phase. This function rotates x
      such that it matches y. Precondition: The columns of x and y differ by a
      multiplicative complex phase factor only.
  
      Args:
        x: `np.ndarray` with Eigenvectors
        y: `np.ndarray` with Eigenvectors
  
      Returns:
        `np.ndarray` containing an equilibrated version of x.
      """
      phases = np.sum(np.conj(x) * y, -2, keepdims=True)
      phases /= np.abs(phases)
      return phases * x
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_linalg_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sort_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling a global function with example usage: EquilibrateEigenVectorPhases(x, y) and returns: unknown