class_docstr: Use a nonlinear regression model with Laplace distributed errors
  as a distribution model
class_docstr_tok: ["Use","a","nonlinear","regression","model","with","Laplace","distributed","errors","as","a","distribution","model"]
code: def __call__(self, H):
      moments = np.array(
          [_L1_moments(H[j, :]) for j in range(H.shape[0])]
      )
      npts_J, mI_J, sI_J = moments[:, 0], moments[:, 1], moments[:, 2]
      hI = np.sum(H, 0)
      hJ = np.sum(H, 1)
      npts, mI, sI = _L1_moments(hI)
      mean_sI_J = np.sum(hJ * sI_J) / nonzero(npts)
      eta2 = 1.0 - mean_sI_J / nonzero(sI)
      if self.renormalize:
          eta2 = correlation2loglikelihood(eta2, npts)
      return eta2
  
imports: import numpy
  from scipy.ndimage import gaussian_filter
  from ._registration import _L1_moments
label_desc: Use image node_id 1 for calling the CorrelationRatioL1 obj's underlying member method code with example usage: obj.__call__(H) and returns: eta2