code: def _compute_gradient_3d(edges, img):
      _, n_y, n_z = img.shape
      gradient = np.abs(
          img[
              edges[0] // (n_y * n_z),
              (edges[0] % (n_y * n_z)) // n_z,
              (edges[0] % (n_y * n_z)) % n_z,
          ]
          - img[
              edges[1] // (n_y * n_z),
              (edges[1] % (n_y * n_z)) // n_z,
              (edges[1] % (n_y * n_z)) % n_z,
          ]
      )
      return gradient
  
imports: from itertools import product
  from numbers import Integral, Number, Real
  import numpy
  from numpy.lib.stride_tricks import as_strided
  from scipy import sparse
  from ..base import BaseEstimator, TransformerMixin, _fit_context
  from ..utils import check_array, check_random_state
  from ..utils._param_validation import Hidden, Interval, RealNotInt, validate_params
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_gradient_3d(edges, img) and returns: gradient