code: def cca(acts1, acts2):
      """
      Calculate a similarity score for two activation matrices using Canonical Correlation Analysis (CCA). Returns the
      average of all the correlation coefficients.
      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: Float between 0 and 1, where 0 means not correlated, 1 means the two activation matrices are linear transformations of each other.
      """
      result = cca_core.get_cca_similarity(
          acts1, acts2, epsilon=1e-10, verbose=False
      )
      return result["mean"][0]
  
imports: from .svcca_lib import cca_core, pwcca, cka_lib
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: cca(acts1, acts2) and returns: result