code: def dtd_covariance(DTD):
      """Calculate covariance of a diffusion tensor distribution (DTD).
  
      Parameters
      ----------
      DTD : numpy.ndarray
          Diffusion tensor distribution of shape (number of tensors, 3, 3) or
          (number of tensors, 6, 1).
  
      Returns
      -------
      C : numpy.ndarray
          Covariance tensor of shape (6, 6).
  
      Notes
      -----
      The covariance tensor is calculated according to the following equation and
      converted into a rank-2 tensor [1]_:
  
          .. math::
  
              \mathbb{C} = \langle \mathbf{D} \otimes \mathbf{D} \rangle -
              \langle \mathbf{D} \rangle \otimes \langle \mathbf{D} \rangle
  
      References
      ----------
      .. [1] Westin, Carl-Fredrik, et al. "Q-space trajectory imaging for
         multidimensional diffusion MRI of the human brain." Neuroimage 135
         (2016): 345-362. https://doi.org/10.1016/j.neuroimage.2016.02.039.
      """
      dims = DTD.shape
      if len(dims) != 3 or (
          dims[1:3] != (3, 3) and dims[1:3] != (6, 1)
      ):
          raise ValueError(
              "The shape of DTD must be (number of tensors, 3, 3) or (number of "
              + "tensors, 6, 1)."
          )
      if dims[1:3] == (3, 3):
          DTD = from_3x3_to_6x1(DTD)
      D = np.mean(DTD, axis=0)
      C = np.mean(
          DTD @ np.swapaxes(DTD, -2, -1), axis=0
      ) - D @ np.swapaxes(D, -2, -1)
      return C
  
imports: from warnings import warn
  import numpy
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import auto_attr
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 7 for calling a global function with example usage: dtd_covariance(DTD) and returns: C