code: def mean_kurtosis_tensor(
      dki_params, min_kurtosis=-3.0 / 7, max_kurtosis=10
  ):
      r"""Compute mean of the kurtosis tensor (MKT) [1]_
  
      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
      min_kurtosis : float (optional)
          To keep kurtosis values within a plausible biophysical range, mean
          kurtosis values that are smaller than `min_kurtosis` are replaced with
          `min_kurtosis`. Default = -3./7 (theoretical kurtosis limit for regions
          that consist of water confined to spherical pores [2]_)
      max_kurtosis : float (optional)
          To keep kurtosis values within a plausible biophysical range, mean
          kurtosis values that are larger than `max_kurtosis` are replaced with
          `max_kurtosis`. Default = 10
      Returns
      -------
      mkt : array
          Calculated mean kurtosis tensor.
  
      Notes
      -----
      The MKT is defined as [1]_:
  
      .. math::
  
           MKT \equiv \frac{1}{4\pi} \int d
           \Omega_{\mathnbf{n}} n_i n_j n_k n_l W_{ijkl}
  
  
      which can be directly computed from the trace of the kurtosis tensor:
  
      .. math::
  
      MKT = \frac{1}{5} Tr(\mathbf{W}) = \frac{1}{5}
      (W_{1111} + W_{2222} + W_{3333} + 2W_{1122} + 2W_{1133} + 2W_{2233})
  
      References
      ----------
      .. [1] Hansen, B., Lund, T. E., Sangill, R., and Jespersen, S. N. (2013).
             Experimentally and computationally fast method for estimation of
             a mean kurtosis.Magnetic Resonance in Medicine69,  1754–1760.388
             doi:10.1002/mrm.24743
      .. [2] Barmpoutis, A., & Zhuo, J., 2011. Diffusion kurtosis imaging:
             Robust estimation from DW-MRI using homogeneous polynomials.
             Proceedings of the 8th {IEEE} International Symposium on
             Biomedical Imaging: From Nano to Macro, ISBI 2011, 262-265.
             doi: 10.1109/ISBI.2011.5872402
  
      """
      MKT = (
          1
          / 5
          * (
              dki_params[..., 12]
              + dki_params[..., 13]
              + dki_params[..., 14]
              + 2 * dki_params[..., 21]
              + 2 * dki_params[..., 22]
              + 2 * dki_params[..., 23]
          )
      )
  
      if min_kurtosis is not None:
          MKT = MKT.clip(min=min_kurtosis)
  
      if max_kurtosis is not None:
          MKT = MKT.clip(max=max_kurtosis)
  
      return MKT
  
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 18 for calling a global function with example usage: mean_kurtosis_tensor(dki_params, min_kurtosis, max_kurtosis) and returns: MKT