docstr: Fitted QTI model.
  
  Parameters
  ----------
  params : numpy.ndarray
      Array of shape (..., 28) containing the model parameters. Element 0
      is the natural logarithm of the signal without diffusion-weighting,
      elements 1-6 are the diffusion tensor elements in Voigt notation,
      and elements 7-27 are the covariance tensor elements in Voigt
      notation.
docstr_tok: ["Fitted","QTI","model",".","Parameters","--","--","--","--","--","params",":","numpy.ndarray","Array","of","shape","(","...",",","28",")","containing","the","model","parameters",".","Element","0","is","the","natural","logarithm","of","the","signal","without","diffusion-weighting",",","elements","1-6","are","the","diffusion","tensor","elements","in","Voigt","notation",",","and","elements","7-27","are","the","covariance","tensor","elements","in","Voigt","notation","."]
code: def __init__(self, params):
      """Fitted QTI model.
  
      Parameters
      ----------
      params : numpy.ndarray
          Array of shape (..., 28) containing the model parameters. Element 0
          is the natural logarithm of the signal without diffusion-weighting,
          elements 1-6 are the diffusion tensor elements in Voigt notation,
          and elements 7-27 are the covariance tensor elements in Voigt
          notation.
      """
      self.params = params
  
imports: from warnings import warn
  import numpy
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import auto_attr
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 1 to create a new QtiFit object  with example: obj = QtiFit(params)