code: def __init__(
      self,
      ddconfig,
      embed_dim,
      lossconfig=None,  # TODO make it configurable
      ckpt_path=None,
      ignore_keys=[],
      image_key="image",
      colorize_nlabels=None,
      monitor=None,
      from_pretrained: str = None,
      capture_cudagraph_iters=-1,
  ):
      super().__init__()
      self.image_key = image_key
      self.encoder = Encoder(**ddconfig)
      self.decoder = Decoder(**ddconfig)
      self.loss = (
          torch.nn.Identity()
      )  # instantiate_from_config(lossconfig)
      assert ddconfig["double_z"]
      self.quant_conv = torch.nn.Conv2d(
          2 * ddconfig["z_channels"], 2 * embed_dim, 1
      )
      self.post_quant_conv = torch.nn.Conv2d(
          embed_dim, ddconfig["z_channels"], 1
      )
      self.embed_dim = embed_dim
      if colorize_nlabels is not None:
          assert type(colorize_nlabels) == int
          self.register_buffer(
              "colorize", torch.randn(3, colorize_nlabels, 1, 1)
          )
      if monitor is not None:
          self.monitor = monitor
      if ckpt_path is not None:
          self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
  
      if from_pretrained is not None:
          state_dict = torch.load(from_pretrained)
          self._load_pretrained_model(state_dict)
  
      # CUDA graph captured sub-modules
      self.capture_cudagraph_iters = capture_cudagraph_iters
      self.stream = torch.cuda.Stream()
      self.encoder_iterations = self.decoder_iterations = 0
      self.encoder_graph = torch.cuda.CUDAGraph()  # eval
      self.decoder_graph = torch.cuda.CUDAGraph()  # eval
      self.graphed_encoder = self.graphed_decoder = None  # train
      self.static_x = self.static_moments = None
      self.static_z = self.static_dec = None
  
imports: from contextlib import contextmanager
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.model import Decoder, Encoder
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config
label_desc: Use image node_id 1 to create a new AutoencoderKL object from inherited base classes: pl with example: obj = AutoencoderKL(ddconfig, embed_dim, lossconfig, ckpt_path, ignore_keys, image_key, colorize_nlabels, monitor, from_pretrained, capture_cudagraph_iters)