class_docstr: Diffusion model which uses the true posterior instead of the prior.
class_docstr_tok: ["Diffusion","model","which","uses","the","true","posterior","instead","of","the","prior","."]
code: def generate_fn(
      self,
      latent: np.ndarray,
      step: int,
      data: Optional[np.ndarray] = None,
  ) -> GaussianParams:
      return super().generate_fn(
          latent=latent, step=step, data=self.data
      )
  
file_path: NeuralCompression/projects/bits_back_diffusion/test.py
imports: from copy import deepcopy
  from typing import Optional, Tuple
  import numpy
  import numpy.random
  import torch
  from bits_back_diffusion.codec import BitsBackCodec, DiffusionModel, GaussianParams, diag_gaussian_unif_bins
  from bits_back_diffusion.script_util import create_gaussian_diffusion
  from craystack import rans
  from craystack.codecs_test import check_codec
label_desc: Use image node_id 2 for calling the CheatingDiffusionModel obj's underlying member method code with example usage: obj.generate_fn(latent, step, data) and returns: super