code: def rate_loss(
      self,
      original: Tensor,
      latent_likelihoods: Tensor,
      quantized_latent_likelihoods: Tensor,
      hyper_latent_likelihoods: Tensor,
      quantized_hyper_latent_likelihoods: Tensor,
  ) -> RateLossOutput:
      # calculate bits-per-pixel for both quantized and noisy quantization
      _, _, height, width = original.shape
      num_pixels = height * width
  
      latent_bpp = (
          self._calc_bits_per_batch(latent_likelihoods) / num_pixels
      )
      quantized_latent_bpp = (
          self._calc_bits_per_batch(quantized_latent_likelihoods)
          / num_pixels
      )
      hyper_bpp = (
          self._calc_bits_per_batch(hyper_latent_likelihoods)
          / num_pixels
      )
      quantized_hyper_bpp = (
          self._calc_bits_per_batch(quantized_hyper_latent_likelihoods)
          / num_pixels
      )
      total_bpp = latent_bpp + hyper_bpp
      quantized_total_bpp = quantized_latent_bpp + quantized_hyper_bpp
  
      # apply rate targeting for loss
      target = self._get_scheduled_rate_param(
          self.target_rate_config.target_bpp,
          self.target_rate_config.target_factors,
          self.target_rate_config.target_steps,
      )
      lam2 = self._get_scheduled_rate_param(
          self.target_rate_config.lam_levels[1],
          self.target_rate_config.lam2_factors,
          self.target_rate_config.lam2_steps,
      )
      lams = torch.where(
          quantized_total_bpp > target,
          lam2,
          self.target_rate_config.lam_levels[0],
      )
  
      return RateLossOutput(
          rate_loss=(lams * total_bpp).mean(),
          total_bpp=total_bpp.detach().mean(),
          quantized_total_bpp=quantized_total_bpp.detach().mean(),
          latent_bpp=latent_bpp.detach().mean(),
          quantized_latent_bpp=quantized_latent_bpp.detach().mean(),
          hyper_bpp=hyper_bpp.detach().mean(),
          quantized_hyper_bpp=quantized_hyper_bpp.detach().mean(),
          target=target,
          lam2=lam2,
      )
  
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 7 for calling the TargetRateCompressionModule obj's underlying member method code with example usage: obj.rate_loss(original, latent_likelihoods, quantized_latent_likelihoods, hyper_latent_likelihoods, quantized_hyper_latent_likelihoods) and returns: RateLossOutput