class_docstr: Class for the Diffusion Kurtosis Model
      
docstr: Predict a signal for this DKI model class instance given parameters
  
  Parameters
  ----------
  dki_params : ndarray (x, y, z, 27) or (n, 27)
  All parameters estimated from the diffusion kurtosis model.
  Parameters are ordered as follows::
  
  1. Three diffusion tensor's eigenvalues
  2. Three lines of the eigenvector matrix each containing the
  first, second and third coordinates of the eigenvector
  3. Fifteen elements of the kurtosis tensor
  
  
  S0 : float or ndarray (optional)
      The non diffusion-weighted signal in every voxel, or across all
      voxels. Default: 1
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code: def predict(self, dki_params, S0=1.0):
      """Predict a signal for this DKI model class instance given parameters
  
      Parameters
      ----------
      dki_params : ndarray (x, y, z, 27) or (n, 27)
      All parameters estimated from the diffusion kurtosis model.
      Parameters are ordered as follows::
  
      1. Three diffusion tensor's eigenvalues
      2. Three lines of the eigenvector matrix each containing the
      first, second and third coordinates of the eigenvector
      3. Fifteen elements of the kurtosis tensor
  
  
      S0 : float or ndarray (optional)
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      """
      return dki_prediction(dki_params, self.gtab, S0)
  
imports: import numpy
  import warnings
  import scipy.optimize
  import dipy.core.sphere
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorFit, mean_diffusivity, from_lower_triangular, lower_triangular, decompose_tensor, MIN_POSITIVE_SIGNAL, nlls_fit_tensor, restore_fit_tensor
  from dipy.reconst.utils import dki_design_matrix
  from dipy.reconst.recspeed import local_maxima
  from dipy.reconst.base import ReconstModel
  from dipy.core.ndindex import ndindex
  from dipy.core.geometry import sphere2cart, cart2sphere, perpendicular_directions
  from dipy.core.optimize import PositiveDefiniteLeastSquares
  from dipy.data import get_sphere, get_fnames, load_sdp_constraints
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.core.gradients import check_multi_b
label_desc: Use image node_id 4 for calling the DiffusionKurtosisModel obj's underlying member method code with example usage: obj.predict(dki_params, S0) and returns: dki_prediction