code: def cka(acts1, acts2):
      """
      Calculates a similarity score for two activation matrices using center kernel alignment (CKA). CKA
      has the benefit of not requiring the number of tokens to be larger than the number of neurons.
  
      Args:
          acts1: Activations matrix #1. 2D numPy array. Dimensions: (neurons, token position)
          acts2: Activations matrix #2. 2D numPy array. Dimensions: (neurons, token position)
  
      Returns:
          score: between 0 and 1, where 0 means not correlated, 1 means the two activation matrices are
          linear transformations of each other.
      """
  
      return cka_lib.feature_space_linear_cka(acts1.T, acts2.T)
  
imports: from .svcca_lib import cca_core, pwcca, cka_lib
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: cka(acts1, acts2) and returns: cka_lib