code: def tsallis(alphas, Ks, dim, required, clamp=True, to_self=False):
      r'''
      Estimate the Tsallis-alpha divergence between distributions, based on kNN
      distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)
  
      If clamp (the default), enforces the estimate is nonnegative.
  
      Returns an array of shape (num_alphas, num_Ks).
      '''
      alphas = np.reshape(alphas, (-1, 1))
      alpha_est = required
  
      est = alpha_est - 1
      est /= alphas - 1
      if clamp:
          np.maximum(est, 0, out=est)
      return esf tsallis(alphas, Ks, dim, required, clamp=True, to_self=False):
      r'''
      Estimate the Tsallis-alpha divergence between distributions, based on kNN
      distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)
  
      If clamp (the default), enforces the estimate is nonnegative.
  
      Returns an array of shape (num_alphas, num_Ks).
      '''
      alphas = np.reshape(alphas, (-1, 1))
      alpha_est = required
  
      est = alpha_est - 1
      est /= alphas - 1
      if clamp:
          np.maximum(est, 0, out=est)
      return est
docs: r'''
      Estimate the Tsallis-alpha divergence between distributions, based on kNN
      distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)
  
      If clamp (the default), enforces the estimate is nonnegative.
  
      Returns an array of shape (num_alphas, num_Ks).