code: def __init__(
      self,
      *,
      # kl
      log_var_init: float = 0.0,
      # common
      kl_weight: float = 1.0,
      d_loss: str = "hinge",
      d_loss_start_step: int = 50001,
      d_num_layers: int = 4,
      d_in_channels: int = 3,
      d_start_channels: int = 64,
      d_factor: float = 1.0,
      d_weight: float = 1.0,
      perceptual_weight: float = 1.0,
  ):
      super().__init__(
          kl_weight=kl_weight,
          d_loss=d_loss,
          d_loss_start_step=d_loss_start_step,
          d_num_layers=d_num_layers,
          d_in_channels=d_in_channels,
          d_start_channels=d_start_channels,
          d_factor=d_factor,
          d_weight=d_weight,
          perceptual_weight=perceptual_weight,
      )
      self.log_var = nn.Parameter(torch.ones(size=()) * log_var_init)
  
imports: import torch
  import numpy
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
label_desc: Use image node_id 1 to create a new AutoEncoderKLLoss object from inherited base classes: AutoEncoderLPIPSWithDiscriminator with example: obj = AutoEncoderKLLoss()