code: def pwcca(acts1, acts2, epsilon=1e-10):
      """
      Calculate a similarity score for two activation matrices using Projection Weighted Canonical Correlation Analysis.
      It's more convenient as it does not require setting a specific number of dims like SVCCA.
      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.
      """
      results = pwcca_lib.compute_pwcca(acts1, acts2, epsilon=epsilon)
      return results[0]
  
imports: from .svcca_lib import cca_core, pwcca, cka_lib
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: pwcca(acts1, acts2, epsilon) and returns: results