content: import importlib
  
  __attributes = {
      'SparseStructureEncoder': 'sparse_structure_vae',
      'SparseStructureDecoder': 'sparse_structure_vae',
      'SparseStructureFlowModel': 'sparse_structure_flow',
      'SLatEncoder': 'structured_latent_vae',
      'SLatGaussianDecoder': 'structured_latent_vae',
      'SLatRadianceFieldDecoder': 'structured_latent_vae',
      'SLatMeshDecoder': 'structured_latent_vae',
      'SLatFlowModel': 'structured_latent_flow',
  }
  
  __submodules = []
  
  __all__ = list(__attributes.keys()) + __submodules
  
  def __getattr__(name):
      if name not in globals():
          if name in __attributes:
              module_name = __attributes[name]
              module = importlib.import_module(f".{module_name}", __name__)
              globals()[name] = getattr(module, name)
          elif name in __submodules:
              module = importlib.import_module(f".{name}", __name__)
              globals()[name] = module
          else:
              raise AttributeError(f"module {__name__} has no attribute {name}")
      return globals()[name]
  
  
  def from_pretrained(path: str, **kwargs):
      """
      Load a model from a pretrained checkpoint.
  
      Args:
          path: The path to the checkpoint. Can be either local path or a Hugging Face model name.
                NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively.
          **kwargs: Additional arguments for the model constructor.
      """
      import os
      import json
      from safetensors.torch import load_file
      is_local = os.path.exists(f"{path}.json") and os.path.exists(f"{path}.safetensors")
  
      if is_local:
          config_file = f"{path}.json"
          model_file = f"{path}.safetensors"
      else:
          from huggingface_hub import hf_hub_download
          path_parts = path.split('/')
          repo_id = f'{path_parts[0]}/{path_parts[1]}'
          model_name = '/'.join(path_parts[2:])
          config_file = hf_hub_download(repo_id, f"{model_name}.json")
          model_file = hf_hub_download(repo_id, f"{model_name}.safetensors")
  
      with open(config_file, 'r') as f:
          config = json.load(f)
      model = __getattr__(config['name'])(**config['args'], **kwargs)
      model.load_state_dict(load_file(model_file))
  
      return model
  
  
  # For Pylance
  if __name__ == '__main__':
      from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder
      from .sparse_structure_flow import SparseStructureFlowModel
      from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatRadianceFieldDecoder, SLatMeshDecoder
      from .structured_latent_flow import SLatFlowModel
  