code: def dkimicro_prediction(params, gtab, S0=1):
      r"""Signal prediction given the DKI microstructure model parameters.
  
      Parameters
      ----------
      params : ndarray (x, y, z, 40) or (n, 40)
      All parameters estimated from the diffusion kurtosis microstructure 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) Six elements of the hindered diffusion tensor
              5) Six elements of the restricted diffusion tensor
              6) Axonal water fraction
      gtab : a GradientTable class instance
          The gradient table for this prediction
      S0 : float or ndarray
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Returns
      -------
      S : (..., N) ndarray
          Simulated signal based on the DKI microstructure model
  
      Notes
      -----
      1) The predicted signal is given by:
      $S(\theta, b) = S_0 * [f * e^{-b ADC_{r}} + (1-f) * e^{-b ADC_{h}]$, where
      $ ADC_{r} and ADC_{h} are the apparent diffusion coefficients of the
      diffusion hindered and restricted compartment for a given direction
      $\theta$, $b$ is the b value provided in the GradientTable input for that
      direction, $f$ is the volume fraction of the restricted diffusion
      compartment (also known as the axonal water fraction).
  
      2) In the original article of DKI microstructural model [1]_, the hindered
      and restricted tensors were defined as the intra-cellular and
      extra-cellular diffusion compartments respectively.
      """
  
      # Initialize pred_sig
      pred_sig = np.zeros(params.shape[:-1] + (gtab.bvals.shape[0],))
  
      # Define dti design matrix and region to process
      D = dti_design_matrix(gtab)
      evals = params[..., :3]
      mask = _positive_evals(
          evals[..., 0], evals[..., 1], evals[..., 2]
      )
  
      # Prepare parameters
      f = params[..., 27]
      adce = params[..., 28:34]
      adci = params[..., 34:40]
  
      if isinstance(S0, np.ndarray):
          S0_vol = S0 * np.ones(params.shape[:-1])
      else:
          S0_vol = S0
  
      # Process pred_sig for all data voxels
      index = ndindex(evals.shape[:-1])
      for v in index:
          if mask[v]:
              pred_sig[v] = (1.0 - f[v]) * np.exp(
                  np.dot(D[:, :6], adce[v])
              ) + f[v] * np.exp(np.dot(D[:, :6], adci[v]))
  
      return pred_sig * S0_vol
  
imports: import numpy
  from dipy.reconst.dti import lower_triangular, from_lower_triangular, decompose_tensor, trace, mean_diffusivity, radial_diffusivity, axial_diffusivity, MIN_POSITIVE_SIGNAL
  from dipy.reconst.dki import split_dki_param, _positive_evals, directional_kurtosis, directional_diffusion, kurtosis_maximum, DiffusionKurtosisModel, DiffusionKurtosisFit
  from dipy.reconst.dti import design_matrix
  from dipy.core.ndindex import ndindex
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.data import get_sphere
  import dipy.core.sphere
label_desc: Use image node_id 3 for calling a global function with example usage: dkimicro_prediction(params, gtab, S0) and returns: unknown