code: def load_model(path: Path = None) -> LatentDiffusion:
      """
      ### Load [`LatentDiffusion` model](latent_diffusion.html)
      """
  
      # Initialize the autoencoder
      with monit.section("Initialize autoencoder"):
          encoder = Encoder(
              z_channels=4,
              in_channels=3,
              channels=128,
              channel_multipliers=[1, 2, 4, 4],
              n_resnet_blocks=2,
          )
  
          decoder = Decoder(
              out_channels=3,
              z_channels=4,
              channels=128,
              channel_multipliers=[1, 2, 4, 4],
              n_resnet_blocks=2,
          )
  
          autoencoder = Autoencoder(
              emb_channels=4,
              encoder=encoder,
              decoder=decoder,
              z_channels=4,
          )
  
      # Initialize the CLIP text embedder
      with monit.section("Initialize CLIP Embedder"):
          clip_text_embedder = CLIPTextEmbedder()
  
      # Initialize the U-Net
      with monit.section("Initialize U-Net"):
          unet_model = UNetModel(
              in_channels=4,
              out_channels=4,
              channels=320,
              attention_levels=[0, 1, 2],
              n_res_blocks=2,
              channel_multipliers=[1, 2, 4, 4],
              n_heads=8,
              tf_layers=1,
              d_cond=768,
          )
  
      # Initialize the Latent Diffusion model
      with monit.section("Initialize Latent Diffusion model"):
          model = LatentDiffusion(
              linear_start=0.00085,
              linear_end=0.0120,
              n_steps=1000,
              latent_scaling_factor=0.18215,
              autoencoder=autoencoder,
              clip_embedder=clip_text_embedder,
              unet_model=unet_model,
          )
  
      # Load the checkpoint
      with monit.section(f"Loading model from {path}"):
          checkpoint = torch.load(path, map_location="cpu")
  
      # Set model state
      with monit.section("Load state"):
          missing_keys, extra_keys = model.load_state_dict(
              checkpoint["state_dict"], strict=False
          )
  
      # Debugging output
      inspect(
          global_step=checkpoint.get("global_step", -1),
          missing_keys=missing_keys,
          extra_keys=extra_keys,
          _expand=True,
      )
  
      #
      model.eval()
      return model
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/diffusion/stable_diffusion/util.py
imports: import os
  import random
  from pathlib import Path
  import PIL
  import numpy
  import torch
  from PIL import Image
  from labml import monit
  from labml.logger import inspect
  from labml_nn.diffusion.stable_diffusion.latent_diffusion import LatentDiffusion
  from labml_nn.diffusion.stable_diffusion.model.autoencoder import Encoder, Decoder, Autoencoder
  from labml_nn.diffusion.stable_diffusion.model.clip_embedder import CLIPTextEmbedder
  from labml_nn.diffusion.stable_diffusion.model.unet import UNetModel
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(path) and returns: model