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: Flatten a message head and tail into a 1d array. This maps to a
  message representation which can be used to measure the message length
  and which can easily be saved to disk.
  
  Returns:
      The 1d numpy array corresponding to the craystack message.
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code: def flat_message(self) -> np.ndarray:
      """
      Flatten a message head and tail into a 1d array. This maps to a
      message representation which can be used to measure the message length
      and which can easily be saved to disk.
  
      Returns:
          The 1d numpy array corresponding to the craystack message.
      """
      return cs.flatten(self.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 3 for calling the BitsBackCodec obj's underlying member method code with example usage: obj.flat_message() and returns: cs