code: def get_saved_model(run_uuid: str, checkpoint: int):
      """
      ### Load [trained large model](large.html)
      """
  
      from labml_nn.distillation.large import Configs as LargeConfigs
  
      # In evaluation mode (no recording)
      experiment.evaluate()
      # Initialize configs of the large model training experiment
      conf = LargeConfigs()
      # Load saved configs
      experiment.configs(conf, experiment.load_configs(run_uuid))
      # Set models for saving/loading
      experiment.add_pytorch_models({"model": conf.model})
      # Set which run and checkpoint to load
      experiment.load(run_uuid, checkpoint)
      # Start the experiment - this will load the model, and prepare everything
      experiment.start()
  
      # Return the model
      return conf.model
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/distillation/__init__.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from labml import experiment, tracker
  from labml.configs import option
  from labml_helpers.train_valid import BatchIndex
  from labml_nn.distillation.large import LargeModel
  from labml_nn.distillation.small import SmallModel
  from labml_nn.experiments.cifar10 import CIFAR10Configs
label_desc: Use image node_id 3 for calling a global function with example usage: get_saved_model(run_uuid, checkpoint) and returns: conf