code: def setup_random_transform(
      transform, rfactor, nslices=45, sigma=1, rng=None
  ):
      r"""Creates a pair of images related to each other by an affine transform
  
      We transform the static image with a random transform so that the
      returned ground-truth transform will produce the static image
      when applied to the moving image. This will simply stack some copies of
      a T1 coronal slice image and add some zero slices up and down to
      reduce boundary artefacts when interpolating.
  
      Parameters
      ----------
      transform: instance of Transform
          defines the type of random transformation that will be created
      rfactor: float
          the factor to multiply the uniform(0,1) random noise that will be
          added to the identity parameters to create the random transform
      nslices: int
          number of slices to be stacked to form the volumes
      sigma: float
          standard deviation of the gaussian filter
      rng: np.random.Generator
          numpy's random generator. If None, it is set with a random seed.
          Default is None
      """
      if rng is None:
          rng = np.random.default_rng()
      dim = 2 if nslices == 1 else 3
      if transform.get_dim() != dim:
          raise ValueError(
              "Transform and requested volume have different dims."
          )
      zero_slices = nslices // 3
  
      fname = get_fnames("t1_coronal_slice")
      moving_slice = np.load(fname)
      moving_slice = moving_slice[40:180, 50:210]
  
      if nslices == 1:
          dim = 2
          moving = moving_slice
          transform_method = vf.transform_2d_affine
      else:
          dim = 3
          transform_method = vf.transform_3d_affine
          moving = np.zeros(shape=moving_slice.shape + (nslices,))
          moving[..., zero_slices : (2 * zero_slices)] = moving_slice[
              ..., None
          ]
  
      moving = sp.ndimage.gaussian_filter(moving, sigma)
  
      moving_g2w = np.eye(dim + 1)
      mmask = np.ones_like(moving, dtype=np.int32)
  
      # Create a transform by slightly perturbing the identity parameters
      theta = transform.get_identity_parameters()
      n = transform.get_number_of_parameters()
      theta += rng.random(n) * rfactor
  
      M = transform.param_to_matrix(theta)
      shape = np.array(moving.shape, dtype=np.int32)
      static = np.array(
          transform_method(moving.astype(np.float32), shape, M)
      )
      static = static.astype(np.float64)
      static_g2w = np.eye(dim + 1)
      smask = np.ones_like(static, dtype=np.int32)
  
      return static, moving, static_g2w, moving_g2w, smask, mmask, M
  
imports: import numpy
  import scipy
  from functools import reduce
  from operator import mul
  from dipy.core.ndindex import ndindex
  from dipy.core.interpolation import interpolate_scalar_2d, interpolate_scalar_3d
  from dipy.data import get_fnames
  from dipy.align import vector_fields
  from dipy.align.transforms import regtransforms
  from dipy.align.parzenhist import ParzenJointHistogram, cubic_spline, cubic_spline_derivative, sample_domain_regular
  from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_almost_equal, assert_equal, assert_raises
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 6 for calling a global function with example usage: setup_random_transform(transform, rfactor, nslices, sigma, rng) and returns: static,  moving,  static_g2w,  moving_g2w,  smask,  mmask,  M