code: def __init__(
      self,
      ddconfig,
      lossconfig,
      n_embed,
      embed_dim,
      ckpt_path=None,
      ignore_keys=None,
      image_key="image",
      colorize_nlabels=None,
      monitor=None,
      batch_resize_range=None,
      scheduler_config=None,
      lr_g_factor=1.0,
      remap=None,
      sane_index_shape=False,  # tell vector quantizer to return indices as bhw
      use_ema=False,
  ):
      super().__init__()
      self.embed_dim = embed_dim
      self.n_embed = n_embed
      self.image_key = image_key
      self.encoder = Encoder(**ddconfig)
      self.decoder = Decoder(**ddconfig)
      self.loss = instantiate_from_config(lossconfig)
      self.quantize = VectorQuantizer(
          n_embed,
          embed_dim,
          beta=0.25,
          remap=remap,
          sane_index_shape=sane_index_shape,
      )
      self.quant_conv = torch.nn.Conv2d(
          ddconfig["z_channels"], embed_dim, 1
      )
      self.post_quant_conv = torch.nn.Conv2d(
          embed_dim, ddconfig["z_channels"], 1
      )
      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
      self.batch_resize_range = batch_resize_range
      if self.batch_resize_range is not None:
          print(
              f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}."
          )
  
      self.use_ema = use_ema
      if self.use_ema:
          self.model_ema = LitEma(self)
          print(
              f"Keeping EMAs of {len(list(self.model_ema.buffers()))}."
          )
  
      if ckpt_path is not None:
          self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
      self.scheduler_config = scheduler_config
      self.lr_g_factor = lr_g_factor
  
imports: import numpy
  import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from torch.optim.lr_scheduler import LambdaLR
  from ldm.modules.ema import LitEma
  from vqvae_quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.util import instantiate_from_config
  import ldm.models.autoencoder
  from packaging import version
label_desc: Use image node_id 1 to create a new VQModel object from inherited base classes: pl with example: obj = VQModel(ddconfig, lossconfig, n_embed, embed_dim, ckpt_path, ignore_keys, image_key, colorize_nlabels, monitor, batch_resize_range, scheduler_config, lr_g_factor, remap, sane_index_shape, use_ema)