code: def get_config():
      """Returns the base experiment configuration for MNIST."""
      config = ml_collections.ConfigDict()
      config.experiment_name = "mnist"
      # Dataset.
      config.dataset_name = "mnist"
      config.dataset_configs = ml_collections.ConfigDict()
      config.data_dtype_str = "float32"
  
      # Model.
      config.model_name = "fully_connected_classification"
      config.model_dtype_str = "float32"
      config.hid_sizes = [64, 64]
      # Training.
      config.trainer_name = "classification_trainer"
  
      config.lr_configs = ml_collections.ConfigDict()
      config.lr_configs.learning_rate_schedule = "compound"
      config.lr_configs.factors = "constant"
      config.lr_configs.base_learning_rate = 0.1
  
      config.optimizer = "momentum"
      config.optimizer_configs = ml_collections.ConfigDict()
      config.optimizer_configs.momentum = 0.9
      config.l2_decay_factor = 0.0005
      config.max_grad_norm = None
      config.label_smoothing = None
      config.num_training_epochs = 10
      config.batch_size = 128
      config.rng_seed = 0
      # Logging.
      config.write_summary = True
      config.xprof = True  # Profile using xprof.
      config.checkpoint = True  # Do checkpointing.
      config.debug_train = False  # Debug mode during training.
      config.debug_eval = False  # Debug mode during eval.
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config() and returns: config