code: def __init__(
      self,
      im4d,
      affine_class=Rigid,
      transforms=None,
      time_interp=True,
      subsampling=(1, 1, 1),
      refscan=0,
      borders=(1, 1, 1),
      optimizer="ncg",
      optimize_template=True,
      xtol=XTOL,
      ftol=FTOL,
      gtol=GTOL,
      stepsize=STEPSIZE,
      maxiter=MAXITER,
      maxfun=MAXFUN,
  ):
      # Check arguments
      check_type_and_shape(subsampling, int, 3)
      check_type(refscan, int, accept_none=True)
      check_type_and_shape(borders, int, 3)
      check_type(xtol, float)
      check_type(ftol, float)
      check_type(gtol, float)
      check_type(stepsize, float)
      check_type(maxiter, int)
      check_type(maxfun, int, accept_none=True)
  
      # Get dimensional parameters
      self.dims = im4d.get_shape()
      self.nscans = self.dims[3]
      # Reduce borders if spatial image dimension too small to avoid
      # getting an empty volume of interest
      borders = [
          min(b, d / 2 - (not d % 2))
          for (b, d) in zip(borders, self.dims[0:3])
      ]
      self.xyz = make_grid(self.dims[0:3], subsampling, borders)
      masksize = self.xyz.shape[0]
      self.data = np.zeros([masksize, self.nscans], dtype="double")
  
      # Initialize space/time transformation parameters
      self.affine = im4d.affine
      self.inv_affine = np.linalg.inv(self.affine)
      if transforms is None:
          self.transforms = [
              affine_class() for scan in range(self.nscans)
          ]
      else:
          self.transforms = transforms
  
      # Compute the 4d cubic spline transform
      self.time_interp = time_interp
      if time_interp:
          self.timestamps = im4d.tr * np.arange(self.nscans)
          self.scanner_time = im4d.scanner_time
          self.cbspline = _cspline_transform(im4d.get_fdata())
      else:
          self.cbspline = np.zeros(self.dims, dtype="double")
          for t in range(self.dims[3]):
              self.cbspline[:, :, :, t] = _cspline_transform(
                  im4d.get_fdata()[:, :, :, t]
              )
  
      # The reference scan conventionally defines the head
      # coordinate system
      self.optimize_template = optimize_template
      if not optimize_template and refscan is None:
          self.refscan = 0
      else:
          self.refscan = refscan
  
      # Set the minimization method
      self.set_fmin(
          optimizer,
          stepsize,
          xtol=xtol,
          ftol=ftol,
          gtol=gtol,
          maxiter=maxiter,
          maxfun=maxfun,
      )
  
      # Auxiliary array for realignment estimation
      self._res = np.zeros(masksize, dtype="double")
      self._res0 = np.zeros(masksize, dtype="double")
      self._aux = np.zeros(masksize, dtype="double")
      self.A = np.zeros(
          (masksize, self.transforms[0].param.size), dtype="double"
      )
      self._pc = None
  
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 1 to create a new Realign4dAlgorithm object  with example: obj = Realign4dAlgorithm(im4d, affine_class, transforms, time_interp, subsampling, refscan, borders, optimizer, optimize_template, xtol, ftol, gtol, stepsize, maxiter, maxfun)