code: def svcca(acts1, acts2, dims: int = 20):
      """
      Calculate a similarity score for two activation matrices using Singular Value Canonical Correlation Analysis
      (SVCCA). A meaningful score requires setting an appropriate value for 'dims', see SVCCA tutorial for how to do
      that.
      Args:
          acts1: Activations matrix #1. 2D numPy array. Dimensions: (neurons, token position)
          acts2: Activations matrix #2. 2D numPy array. Dimensions: (neurons, token position)
          dims: The number of dimensions to consider for SVCCA calculation. See the SVCCA tutorial to see how to
                  determine this in a way
  
      Returns:
          score: between 0 and 1, where 0 means not correlated, 1 means the two activation matrices are linear
          transformations of each other.
      """
      # Center activations by subtracting the mean
      centered_acts_1 = acts1 - np.mean(acts1, axis=1, keepdims=True)
      centered_acts_2 = acts2 - np.mean(acts2, axis=1, keepdims=True)
  
      # Perform SVD
      _, s1, v1 = np.linalg.svd(centered_acts_1, full_matrices=False)
      _, s2, v2 = np.linalg.svd(centered_acts_2, full_matrices=False)
  
      # Reconstruct by multiplying s and v but only for the top <dims> number of dimensions
      sv_acts1 = np.dot(s1[:dims] * np.eye(dims), v1[:dims])
      sv_acts2 = np.dot(s2[:dims] * np.eye(dims), v2[:dims])
  
      # NOW do cca to get SVCCA values
      results = cca_core.get_cca_similarity(
          sv_acts1, sv_acts2, epsilon=1e-10, verbose=False
      )
  
      return np.mean(results["cca_coef1"])
  
imports: from .svcca_lib import cca_core, pwcca, cka_lib
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: svcca(acts1, acts2, dims) and returns: np