code: def __init__(
      self,
      disc_start: int,
      logvar_init: float = 0.0,
      disc_num_layers: int = 3,
      disc_in_channels: int = 3,
      disc_factor: float = 1.0,
      disc_weight: float = 1.0,
      perceptual_weight: float = 1.0,
      disc_loss: str = "hinge",
      scale_input_to_tgt_size: bool = False,
      dims: int = 2,
      learn_logvar: bool = False,
      regularization_weights: Union[None, Dict[str, float]] = None,
      additional_log_keys: Optional[List[str]] = None,
      discriminator_config: Optional[Dict] = None,
  ):
      super().__init__()
      self.dims = dims
      if self.dims > 2:
          print(
              f"running with dims={dims}. This means that for perceptual loss "
              f"calculation, the LPIPS loss will be applied to each frame "
              f"independently."
          )
      self.scale_input_to_tgt_size = scale_input_to_tgt_size
      assert disc_loss in ["hinge", "vanilla"]
      self.perceptual_loss = LPIPS().eval()
      self.perceptual_weight = perceptual_weight
      # output log variance
      self.logvar = nn.Parameter(
          torch.full((), logvar_init), requires_grad=learn_logvar
      )
      self.learn_logvar = learn_logvar
  
      discriminator_config = default(
          discriminator_config,
          {
              "target": "sgm.modules.autoencoding.lpips.model.model.NLayerDiscriminator",
              "params": {
                  "input_nc": disc_in_channels,
                  "n_layers": disc_num_layers,
                  "use_actnorm": False,
              },
          },
      )
  
      self.discriminator = instantiate_from_config(
          discriminator_config
      ).apply(weights_init)
      self.discriminator_iter_start = disc_start
      self.disc_loss = (
          hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
      )
      self.disc_factor = disc_factor
      self.discriminator_weight = disc_weight
      self.regularization_weights = default(regularization_weights, {})
  
      self.forward_keys = [
          "optimizer_idx",
          "global_step",
          "last_layer",
          "split",
          "regularization_log",
      ]
  
      self.additional_log_keys = set(default(additional_log_keys, []))
      self.additional_log_keys.update(
          set(self.regularization_weights.keys())
      )
  
imports: from typing import Dict, Iterator, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torchvision
  from einops import rearrange
  from matplotlib import colormaps
  from matplotlib import pyplot
  from ....util import default, instantiate_from_config
  from ..lpips.loss.lpips import LPIPS
  from ..lpips.model.model import weights_init
  from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
label_desc: Use image node_id 1 to create a new GeneralLPIPSWithDiscriminator object from inherited base classes: nn with example: obj = GeneralLPIPSWithDiscriminator(disc_start, logvar_init, disc_num_layers, disc_in_channels, disc_factor, disc_weight, perceptual_weight, disc_loss, scale_input_to_tgt_size, dims, learn_logvar, regularization_weights, additional_log_keys, discriminator_config)