code: def _init_energy(self, pc):
      if pc is self._pc:
          return
      self.set_transform(self._t, pc)
      self._pc = pc
      self._res[:] = self.data[:, self._t] - self.mu[:]
      self._V = np.maximum(self.offset + np.mean(self._res**2), SMALL)
      self._res0[:] = self.data[:, self._t] - self.mu0
      self._V0 = np.maximum(
          self.offset0 + np.mean(self._res0**2), SMALL
      )
  
      if self.use_derivatives:
          # linearize the data wrt the transform parameters
          # use the auxiliary array to save the current resampled data
          self._aux[:] = self.data[:, self._t]
          basis = np.eye(6)
          for j in range(pc.size):
              self.set_transform(self._t, pc + self.stepsize * basis[j])
              self.A[:, j] = (
                  self.data[:, self._t] - self._aux
              ) / self.stepsize
          self.transforms[self._t].param = pc
          self.data[:, self._t] = self._aux[:]
          # pre-compute gradient and hessian of numerator and
          # denominator
          c = 2 / float(self.data.shape[0])
          self._dV = c * np.dot(self.A.T, self._res)
          self._dV0 = c * np.dot(self.A.T, self._res0)
          self._H = c * np.dot(self.A.T, self.A)
  
imports: import os
  import warnings
  import numpy
  from nibabel import io_orientation
  from nibabel.affines import apply_affine
  from ...core.image.image_spaces import as_xyz_image, make_xyz_image, xyz_affine
  from ...io.nibcompat import get_header
  from ..slicetiming import timefuncs
  from ._registration import _cspline_sample3d, _cspline_sample4d, _cspline_transform
  from .affine import Affine, Rigid
  from .optimizer import configure_optimizer, use_derivatives
  from .type_check import check_type, check_type_and_shape
label_desc: Use image node_id 7 for calling the Realign4dAlgorithm obj's underlying member method code with example usage: obj._init_energy(pc) without return types