code: def reconstruct_with_vqgan(x, model):
      z, _, [_, _, indices] = model.encode(x)
      print(
          f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"
      )
      xrec = model.decode(z)
      return xrec
  
file_path: transformers/examples/research_projects/vqgan-clip/loaders.py
imports: import importlib
  import torch
  import yaml
  from omegaconf import OmegaConf
  from taming.models.vqgan import VQModel
label_desc: Use image node_id 3 for calling a global function with example usage: reconstruct_with_vqgan(x, model) and returns: xrec