code: def __init__(
      self,
      img_size: int,
      in_channels: int,
      out_channels: int,
      inner_channels: int,
      latent_channels: int,
      channel_multipliers: Tuple[int, ...],
      *,
      embedding_channels: int,
      num_res_blocks: int,
      attention_resolutions: Tuple[int, ...] = (),
      dropout: float = 0.0,
      resample_with_conv: bool = True,
      attention_type: str = "spatial",
      # loss configs
      use_loss: bool = True,
      kl_weight: float = 1.0,
      log_var_init: float = 0.0,
      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,
      # train step configs
      grad_accumulate: int = 1,
  ):
      super().__init__(
          img_size,
          in_channels,
          out_channels,
          inner_channels,
          latent_channels,
          channel_multipliers,
          embedding_channels=embedding_channels,
          num_res_blocks=num_res_blocks,
          attention_resolutions=attention_resolutions,
          dropout=dropout,
          resample_with_conv=resample_with_conv,
          attention_type=attention_type,
      )
      if not use_loss:
          self.loss = None
      else:
          self.loss = AutoEncoderKLLoss(
              kl_weight=kl_weight,
              log_var_init=log_var_init,
              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.setup(
          img_size,
          grad_accumulate,
          embedding_channels,
          channel_multipliers,
      )
  
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 AutoEncoderKLModel object from inherited base classes: AutoEncoderKL, IAutoEncoder with example: obj = AutoEncoderKLModel(img_size, in_channels, out_channels, inner_channels, latent_channels, channel_multipliers)