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: Output the state of the codec, e.g., for saving.
  
  Returns:
      A dict containing the flattened message, the data count,
      and the extra/push/pop/initial bytes.
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code: def state_dict(self) -> dict:
      """
      Output the state of the codec, e.g., for saving.
  
      Returns:
          A dict containing the flattened message, the data count,
          and the extra/push/pop/initial bytes.
      """
      return {
          "flat_message": self.flat_message,
          "data_count": self.data_count,
          "extr_bytes": self.extra_bytes,
          "push_bytes": self.push_bytes,
          "pop_bytes": self.pop_bytes,
          "initial_bytes": self.initial_bytes,
      }
  
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 19 for calling the BitsBackCodec obj's underlying member method code with example usage: obj.state_dict() and returns: dict