code: def __init__(
      self,
      ddconfig,
      lossconfig,
      embed_dim,
      ckpt_path=None,
      ignore_keys=[],
      image_key="image",
      colorize_nlabels=None,
      monitor=None,
  ):
      super().__init__()
      self.image_key = image_key
      self.encoder = Encoder(**ddconfig)
      self.decoder = Decoder(**ddconfig)
      self.loss = 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)
  
imports: import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from taming.modules.vqvae.quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
  from ldm.util 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, lossconfig, embed_dim, ckpt_path, ignore_keys, image_key, colorize_nlabels, monitor)