class_docstr: A class to represent a generic intensity-based image registration
  algorithm.
class_docstr_tok: ["A","class","to","represent","a","generic","intensity-based","image","registration","algorithm","."]
docstr: Evaluate similarity function given a world-to-world transform.
  
  Parameters
  ----------
  T : Transform
      Transform object implementing ``apply`` method
docstr_tok: ["Evaluate","similarity","function","given","a","world-to-world","transform",".","Parameters","--","--","--","--","--","T",":","Transform","Transform","object","implementing","``","apply","``","method"]
code: def eval(self, T):
      """
      Evaluate similarity function given a world-to-world transform.
  
      Parameters
      ----------
      T : Transform
          Transform object implementing ``apply`` method
      """
      Tv = ChainTransform(
          T, pre=self._from_affine, post=self._to_inv_affine
      )
      return self._eval(Tv)
  
imports: import numpy
  import scipy.ndimage
  from ...core.image.image_spaces import as_xyz_image, make_xyz_image, xyz_affine
  from ._registration import _joint_histogram
  from .affine import affine_transforms, inverse_affine, subgrid_affine
  from .chain_transform import ChainTransform
  from .optimizer import configure_optimizer
  from .similarity_measures import similarity_measures
label_desc: Use image node_id 9 for calling the HistogramRegistration obj's underlying member method code with example usage: obj.eval(T) and returns: self