code: def __init__(
      self,
      model: HyperpriorAutoencoderBase,
      target_rate_config: TargetRateConfig,
      optimizer_config: DictConfig,
      distortion_lam: float = 1.0,
      distortion_loss: Optional[DistortionLoss] = None,
      gradient_clip_val: Optional[float] = None,
      gradient_clip_algorithm: str = "norm",
  ):
      super().__init__()
      self.model: HyperpriorAutoencoderBase = model
      self.target_rate_config = target_rate_config
      self.optimizer_config = optimizer_config
      self.distortion_lam = distortion_lam
      self.train_dataset: Optional[Dataset] = None
      self.eval_dataset: Optional[Dataset] = None
  
      self.gradient_clip_val = gradient_clip_val
      self.gradient_clip_algorithm = gradient_clip_algorithm
  
      if distortion_loss is None:
          distortion_loss = MSELoss()
      self.distortion_loss = distortion_loss
  
      self.automatic_optimization = False
  
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 1 to create a new TargetRateCompressionModule object from inherited base classes: ImageModule with example: obj = TargetRateCompressionModule(model, target_rate_config, optimizer_config, distortion_lam, distortion_loss, gradient_clip_val, gradient_clip_algorithm)