code: def get_model_dataset(
      run_uuid: str,
  ) -> Tuple[AutoregressiveTransformer, TextDataset]:
      experiment.evaluate()
  
      conf = Configs()
  
      experiment.configs(conf, experiment.load_configs(run_uuid))
  
      experiment.load(run_uuid)
  
      experiment.add_pytorch_models({"model": conf.model})
  
      experiment.start()
  
      return conf.model, conf.text
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/sampling/experiment_tiny.py
imports: from typing import Tuple
  import torch
  from labml import experiment, monit
  from labml import logger
  from labml.logger import Text
  from labml_helpers.datasets.text import TextDataset
  from labml_nn.sampling import Sampler
  from labml_nn.sampling.greedy import GreedySampler
  from labml_nn.sampling.nucleus import NucleusSampler
  from labml_nn.sampling.temperature import TemperatureSampler
  from labml_nn.sampling.top_k import TopKSampler
  from labml_nn.transformers.basic.autoregressive_experiment import Configs, AutoregressiveTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_dataset(run_uuid) and returns: conf,  conf