class_docstr: Class for the Correlation Tensor Model
      
class_docstr_tok: ["Class","for","the","Correlation","Tensor","Model"]
docstr: Fit method of the CTI model class.
  
  Parameters
  ----------
  data : array
      4D array of dMRI data.
  
  mask : array, optional
      A boolean array of the same shape as data.shape[-1]. It
      designates which coordinates in the data should be analyzed.
docstr_tok: ["Fit","method","of","the","CTI","model","class",".","Parameters","--","--","--","--","--","data",":","array","4D","array","of","dMRI","data",".","mask",":","array",",","optional","A","boolean","array","of","the","same","shape","as","data.shape","[","-1","]",".","It","designates","which","coordinates","in","the","data","should","be","analyzed","."]
code: def fit(self, data, mask=None):
      """Fit method of the CTI model class.
  
      Parameters
      ----------
      data : array
          4D array of dMRI data.
  
      mask : array, optional
          A boolean array of the same shape as data.shape[-1]. It
          designates which coordinates in the data should be analyzed.
      """
      data_thres = np.maximum(data, self.min_signal)
      params = self.fit_method(
          self.design_matrix,
          data_thres,
          self.inverse_design_matrix,
          weights=self.weights,
          *self.args,
          **self.kwargs,
      )
  
      return CorrelationTensorFit(self, params)
  
imports: import numpy
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.utils import cti_design_matrix
  from dipy.reconst.dki import DiffusionKurtosisFit
  from dipy.reconst.dti import decompose_tensor, from_lower_triangular, lower_triangular, MIN_POSITIVE_SIGNAL
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 2 for calling the CorrelationTensorModel obj's underlying member method code with example usage: obj.fit(data, mask) and returns: CorrelationTensorFit