code: def __init__(
      self,
      model: HyperpriorAutoencoderBase,
      target_rate_config: TargetRateConfig,
      discriminator: nn.Module,
      discriminator_loss: DiscriminatorLoss,
      generator_loss: GeneratorLoss,
      optimizer_config: DictConfig,
      distortion_lam: float = 1.0,
      distortion_loss: Optional[DistortionLoss] = None,
      latent_projector: Optional[nn.Module] = None,
      generator_weight: float = 1.0,
      gradient_clip_val: Optional[float] = None,
      gradient_clip_algorithm: str = "norm",
      mc_sampling: bool = True,
  ):
      super().__init__(
          model=model,
          target_rate_config=target_rate_config,
          optimizer_config=optimizer_config,
          distortion_lam=distortion_lam,
          distortion_loss=distortion_loss,
          gradient_clip_val=gradient_clip_val,
          gradient_clip_algorithm=gradient_clip_algorithm,
      )
  
      self.discriminator = discriminator
      self.discriminator_loss = discriminator_loss
      self.latent_projector = latent_projector
      self.generator_loss = generator_loss
      self.generator_weight = generator_weight
      self.mc_sampling = mc_sampling
  
imports: from typing import Optional, Tuple
  import hydra
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from target_compression_module import TargetRateCompressionModule
  from torch import Tensor
  from neuralcompression import HyperpriorOutput
  from neuralcompression.loss_fn import DiscriminatorLoss, DistortionLoss, GeneratorLoss, TargetRateConfig
  from neuralcompression.models import HyperpriorAutoencoderBase
label_desc: Use image node_id 1 to create a new GANCompressionModule object from inherited base classes: TargetRateCompressionModule with example: obj = GANCompressionModule(model, target_rate_config, discriminator, discriminator_loss, generator_loss, optimizer_config, distortion_lam, distortion_loss, latent_projector, generator_weight, gradient_clip_val, gradient_clip_algorithm, mc_sampling)