code: def multi_gaussian_k_from_c(ccti, MD):
      """
      Computes the multiple Gaussian diffusion kurtosis tensor from the
      covariance tensor.
  
      Parameters
      ----------
      ccti: array(..., 21)
          Covariance Tensor Elements with no hidden factors.
      MD: Mean Diffusivity (MD) of a diffusion tensor.
  
      Returns
      -------
      K: array (..., 15)
          Fifteen elements of the kurtosis tensor
      """
      K = np.zeros((15, 1))
      K[0] = 3 * ccti[0] / (MD**2)
      K[1] = 3 * ccti[1] / (MD**2)
      K[2] = 3 * ccti[2] / (MD**2)
      K[3] = 3 * ccti[8] / (MD**2)
      K[4] = 3 * ccti[7] / (MD**2)
      K[5] = 3 * ccti[11] / (MD**2)
      K[6] = 3 * ccti[9] / (MD**2)
      K[7] = 3 * ccti[13] / (MD**2)
      K[8] = 3 * ccti[12] / (MD**2)
      K[9] = (ccti[5] + 2 * ccti[17]) / (MD**2)
      K[10] = (ccti[4] + 2 * ccti[16]) / (MD**2)
      K[11] = (ccti[3] + 2 * ccti[15]) / (MD**2)
      K[12] = (ccti[6] + 2 * ccti[19]) / (MD**2)
      K[13] = (ccti[10] + 2 * ccti[20]) / (MD**2)
      K[14] = (ccti[14] + 2 * ccti[18]) / (MD**2)
      return K
  
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 2 for calling a global function with example usage: multi_gaussian_k_from_c(ccti, MD) and returns: K