code: def cka(gram_x, gram_y, debiased=False):
      """Compute CKA.
  
      Args:
        gram_x: A num_examples x num_examples Gram matrix.
        gram_y: A num_examples x num_examples Gram matrix.
        debiased: Use unbiased estimator of HSIC. CKA may still be biased.
  
      Returns:
        The value of CKA between X and Y.
      """
      gram_x = center_gram(gram_x, unbiased=debiased)
      gram_y = center_gram(gram_y, unbiased=debiased)
  
      # Note: To obtain HSIC, this should be divided by (n-1)**2 (biased variant) or
      # n*(n-3) (unbiased variant), but this cancels for CKA.
      scaled_hsic = gram_x.ravel().dot(gram_y.ravel())
  
      normalization_x = np.linalg.norm(gram_x)
      normalization_y = np.linalg.norm(gram_y)
      return scaled_hsic / (normalization_x * normalization_y)
  
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: cka(gram_x, gram_y, debiased) and returns: unknown