class_docstr: Lossless compression for Herarchical latent variable models.
  
  Based on the Craystack (https://github.com/j-towns/craystack)
  implementation of Bits-Back coding.
  
  Args:
      model: The hierarchical latent variable model.
      data_shape: The shape [batch-size, channels, height, width]
          of the data and each latent.
      latent_prec: Coding precision.
      prior_prec: Precision for the discretization.
      obs_prec: Precision for the observation codec.
      data_prec: Precision of the discrete data.
      initial_len: Length of the random initial bitstring.
      data_min: Minimum value of the data to be fed into the model.
      data_max: Maximum value of the data to be fed into the model.
      clip_stdd: Minimum value for the stdd of the observation codec.
      track: Whether to track the message sizes at each step.
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docstr: Push indices idx_1, ..., idx_{steps-1} bottom-up
  using a uniform distribution.
  
  Args:
      message: The craystack message to push to.
      discr_latents: The list of discretized latents
          to be pushed to the message.
  Returns:
      The new craystack message with the indices appended.
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code: def prior_push(
      self, message: tuple, discr_latents: List[DiscrLatent]
  ) -> tuple:
      """
      Push indices idx_1, ..., idx_{steps-1} bottom-up
      using a uniform distribution.
  
      Args:
          message: The craystack message to push to.
          discr_latents: The list of discretized latents
              to be pushed to the message.
      Returns:
          The new craystack message with the indices appended.
      """
      assert len(discr_latents) == self.model.steps
      for step in range(1, self.model.steps + 1):
          codec = self._track_codec(self.prior_codec, step=step)
          (message,) = codec.push(message, discr_latents[step - 1].inds)
      return (message,)
  
imports: from abc import ABC, abstractmethod
  from typing import List, NamedTuple, Optional, Tuple, Union, overload
  import craystack
  import numpy
  import torch
  from craystack import codecs
  from craystack.bb_ans import BBANS
  from improved_diffusion.gaussian_diffusion import GaussianDiffusion
label_desc: Use image node_id 10 for calling the BitsBackCodec obj's underlying member method code with example usage: obj.prior_push(message, discr_latents) and returns: message