code: def ls_fit_cti(
      design_matrix,
      data,
      inverse_design_matrix,
      weights=True,
      min_diffusivity=0,
  ):
      r"""Compute the diffusion kurtosis and covariance tensors using an
      ordinary or weighted linear least squares approach
  
      Parameters
      ----------
      design_matrix : array (g, 43)
          Design matrix holding the covariants used to solve for the regression
          coefficients.
      data : array (g)
          Data or response variables holding the data.
      inverse_design_matrix : array (43, g)
          Inverse of the design matrix.
      weights : bool, optional
          Parameter indicating whether weights are used. Default: True.
      min_diffusivity : float, optional
          Because negative eigenvalues are not physical and small eigenvalues,
          much smaller than the diffusion weighting, cause quite a lot of noise
          in metrics such as fa, diffusivity values smaller than `min_diffusivity`
          are replaced with `min_diffusivity`.
  
      Returns
      -------
      cti_params : array (48)
      All parameters estimated from the diffusion kurtosis model for all N
      voxels. Parameters are ordered as follows:
          1) Three diffusion tensor eigenvalues.
          2) Three blocks of three elements, containing the first second and
              third coordinates of the diffusion tensor eigenvectors.
          3) Fifteen elements of the kurtosis tensor.
          4) Twenty One elements of the covariance tensor.
  
      """
      A = design_matrix
      y = np.log(data)
      result = np.dot(inverse_design_matrix, y)
      if weights:
          W = np.diag(np.exp(2 * np.dot(A, result)))
          AT_W = np.dot(A.T, W)
          inv_AT_W_A = np.linalg.pinv(np.dot(AT_W, A))
          AT_W_LS = np.dot(AT_W, y)
          result = np.dot(inv_AT_W_A, AT_W_LS)
      cti_params = params_to_cti_params(
          result, min_diffusivity=min_diffusivity
      )
  
      return cti_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 6 for calling a global function with example usage: ls_fit_cti(design_matrix, data, inverse_design_matrix, weights, min_diffusivity) and returns: cti_params