code: def _diff_msk_from_awf(f, msk):
      """
      Helper function that calculates differential of function msk_from_awf
  
      Parameters
      ----------
      f : ndarray ([X, Y, Z, ...])
          ndarray containing the axonal volume fraction estimate.
  
      Returns
      -------
      dkdf : ndarray(nub)
          Mean signal kurtosis differential
      msk : float
          Measured mean signal kurtosis.
  
      Notes
      -----
      This function corresponds to the differential of equations 17 of [1]_.
      This function is applicable to both _msk_from_awf and _msk_from_awf_error.
  
      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
      """
      F = (
          216 * f - 504 * f**2 + 504 * f**3 - 180 * f**4
      )  # Numerator
      G = (
          135 - 360 * f + 420 * f**2 - 240 * f**3 + 60 * f**4
      )  # Denominator
  
      dF = (
          216 - 1008 * f + 1512 * f**2 - 720 * f**3
      )  # Num. differential
      dG = (
          -360 + 840 * f - 720 * f**2 + 240 * f**3
      )  # Den. differential
  
      return (G * dF - F * dG) / (G**2)
  
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 4 for calling a global function with example usage: _diff_msk_from_awf(f, msk) and returns: unknown