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: Compute the latent from indices corresponding to bins
  having equal mass under Gaussian priors.
  
  Args:
      discr_latent: The discretized latent specifying the indices and
          the parameters (mean and stdd) of the Gaussian prior.
  Returns:
      The bins corresponding to the latent.
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code: def _to_latent(self, discr_latent: DiscrLatent) -> np.ndarray:
      """
      Compute the latent from indices corresponding to bins
      having equal mass under Gaussian priors.
  
      Args:
          discr_latent: The discretized latent specifying the indices and
              the parameters (mean and stdd) of the Gaussian prior.
      Returns:
          The bins corresponding to the latent.
      """
      params = discr_latent.params
      return (
          params.mean
          + cs.std_gaussian_centres(self.prior_prec)[discr_latent.inds]
          * params.stdd
      )
  
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 8 for calling the BitsBackCodec obj's underlying member method code with example usage: obj._to_latent(discr_latent) and returns: unknown