class_docstr: Use a nonlinear regression model with Gaussian errors as a
  distribution model
class_docstr_tok: ["Use","a","nonlinear","regression","model","with","Gaussian","errors","as","a","distribution","model"]
code: def __call__(self, H):
      npts_J = np.sum(H, 1)
      tmp = nonzero(npts_J)
      mI_J = np.sum(H * self.I, 1) / tmp
      vI_J = np.sum(H * (self.I) ** 2, 1) / tmp - mI_J**2
      npts = np.sum(npts_J)
      tmp = nonzero(npts)
      hI = np.sum(H, 0)
      hJ = np.sum(H, 1)
      mI = np.sum(hI * self.I[0, :]) / tmp
      vI = np.sum(hI * self.I[0, :] ** 2) / tmp - mI**2
      mean_vI_J = np.sum(hJ * vI_J) / tmp
      eta2 = 1.0 - mean_vI_J / nonzero(vI)
      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 CorrelationRatio obj's underlying member method code with example usage: obj.__call__(H) and returns: eta2