code: def cti_prediction(cti_params, gtab1, gtab2, S0=1):
      """Predict a signal given correlation tensor imaging parameters.
  
      Parameters
      ----------
      cti_params: numpy.ndarray (..., 48)
      All parameters estimated from the correlation tensor 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
          4. Twenty-One elements of the covariance tensor
      gtab1: dipy.core.gradients.GradientTable
          A GradientTable class instance for first DDE diffusion epoch
  
      gtab2: dipy.core.gradients.GradientTable
          A GradientTable class instance for second DDE diffusion epoch
  
      S0 : float or ndarray (optional)
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Returns
      -------
      S : ndarray
          Simulated signal based on the CTI model
  
      """
      evals, evecs, kt, ct = split_cti_params(cti_params)
      A = design_matrix(gtab1, gtab2)
      fevals = evals.reshape((-1, evals.shape[-1]))
      fevecs = evecs.reshape((-1,) + evecs.shape[-2:])
      fct = ct.reshape((-1, ct.shape[-1]))
      fkt = kt.reshape((-1, kt.shape[-1]))
      pred_sig = np.zeros((len(fevals), len(gtab1.bvals)))
  
      if isinstance(S0, np.ndarray):
          S0_vol = np.reshape(S0, (len(fevals)))
      else:
          S0_vol = S0
      for v in range(len(pred_sig)):
          DT = np.dot(
              np.dot(fevecs[v], np.diag(fevals[v])), fevecs[v].T
          )
          dt = lower_triangular(DT)
          MD = (dt[0] + dt[2] + dt[5]) / 3
          if isinstance(S0_vol, np.ndarray):
              this_S0 = S0_vol[v]
          else:
              this_S0 = S0_vol
          X = np.concatenate(
              (
                  dt,
                  fkt[v] * MD * MD,
                  fct[v],
                  np.array([-np.log(this_S0)]),
              ),
              axis=0,
          )
          pred_sig[v] = np.exp(np.dot(A, X))
      pred_sig = pred_sig.reshape(
          cti_params.shape[:-1] + (pred_sig.shape[-1],)
      )
  
      return pred_sig
  
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 4 for calling a global function with example usage: cti_prediction(cti_params, gtab1, gtab2, S0) and returns: pred_sig