code: def msk_from_awf(f):
      """
      Computes mean signal kurtosis from axonal water fraction estimates of the
      SMT2 model
  
      Parameters
      ----------
      f : ndarray ([X, Y, Z, ...])
          ndarray containing the axonal volume fraction estimate.
  
      Returns
      -------
      msk : ndarray(nub)
          Mean signal kurtosis (msk)
  
      Notes
      -----
      Computes mean signal kurtosis using equations 17 of [1]_
  
      References
      ----------
      .. [1] Neto Henriques R, Jespersen SN, Shemesh N (2019). Microscopic
             anisotropy misestimation in spherical‐mean single diffusion
             encoding MRI. Magnetic Resonance in Medicine (In press).
             doi: 10.1002/mrm.27606
      """
      msk_num = 216 * f - 504 * f**2 + 504 * f**3 - 180 * f**4
      msk_den = (
          135 - 360 * f + 420 * f**2 - 240 * f**3 + 60 * f**4
      )
      msk = msk_num / msk_den
  
      return msk
  
imports: import numpy
  import scipy.optimize
  from dipy.core.gradients import check_multi_b, unique_bvals_magnitude, round_bvals
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import MIN_POSITIVE_SIGNAL
  from dipy.core.ndindex import ndindex
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 2 for calling a global function with example usage: msk_from_awf(f) and returns: msk