code: def _grad(M):
      """
      Computes one way first difference
      """
      x_ind = list(range(1, M.shape[0])) + [M.shape[0] - 1]
      y_ind = list(range(1, M.shape[1])) + [M.shape[1] - 1]
      z_ind = list(range(1, M.shape[2])) + [M.shape[2] - 1]
  
      grad = np.zeros((*M.shape[:3], 3, M.shape[-1]), dtype=np.float32)
      grad[:, :, :, 0, :] = M[x_ind, :, :, :] - M
      grad[:, :, :, 1, :] = M[:, y_ind, :, :] - M
      grad[:, :, :, 2, :] = M[:, :, z_ind, :] - M
  
      return grad
  
imports: import logging
  import warnings
  import numpy
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.core.gradients import gradient_table, unique_bvals_tolerance, get_bval_indices
  from dipy.core.onetime import auto_attr
  from dipy.core.sphere import Sphere
  from dipy.data import get_sphere
  from dipy.reconst.shm import lazy_index, normalize_data
  from dipy.reconst.odf import OdfModel, OdfFit
  from dipy.reconst.csdeconv import AxSymShResponse
  from dipy.segment.mask import bounding_box, crop
  from dipy.sims.voxel import single_tensor, all_tensor_evecs
label_desc: Use image node_id 5 for calling a global function with example usage: _grad(M) and returns: grad