code: def _calc_bits_per_batch(self, likelihoods: Tensor) -> Tensor:
      batch_size = likelihoods.shape[0]
      likelihoods = likelihoods.view(batch_size, -1)
      return likelihoods.log().sum(1) / -math.log(2)
  
imports: import math
  from typing import List, NamedTuple, Optional
  import hydra
  import torch
  from image_module import ImageModule
  from omegaconf import DictConfig
  from torch import Tensor
  from torch.utils.data import Dataset
  from neuralcompression import HyperpriorOutput
  from neuralcompression.loss_fn import DistortionLoss, MSELoss, TargetRateConfig
  from neuralcompression.metrics import pickle_size_of
  from neuralcompression.models import HyperpriorAutoencoderBase
label_desc: Use image node_id 6 for calling the TargetRateCompressionModule obj's underlying member method code with example usage: obj._calc_bits_per_batch(likelihoods) and returns: unknown