code: def get_config(config_name: str) -> ml_collections.ConfigDict:
      """Training parameters."""
      config = ml_collections.ConfigDict()
  
      config.model_type = "inception_v3"
      config.trial = 0  # Used to allow for replicates during training.
  
      # Default Dataset
      config.train_dataset_pbtxt = (
          "/path/to/your/train.dataset_config.pbtxt"
      )
      config.tune_dataset_pbtxt = (
          "/path/to/your/tune.dataset_config.pbtxt"
      )
  
      config.best_checkpoint_metric = "tune/f1_weighted"
      config.batch_size = 16384
      config.num_epochs = 10
      config.num_validation_examples = 1500000
      config.optimizer = "rmsprop"
  
      # Training hyperparameters
      config.learning_rate = 0.001
      config.learning_rate_num_epochs_per_decay = 2.0
      config.learning_rate_decay_rate = 0.947
      config.average_decay = 0.999
      config.label_smoothing = 1e-6
      config.rho = 0.9
      config.momentum = 0.9
      config.epsilon = 1.0
      config.warmup_steps = 10_000
      config.init_checkpoint = ""
      config.init_backbone_with_imagenet = False
      config.best_metrics = "tune/f1_weighted"
      config.weight_decay = 0.00004
      config.backbone_dropout_rate = 0.2
      # Stop training when this many consecutive evaluations yield no improvement.
      config.early_stopping_patience = 10
  
      # TensorBoard Options
      config.log_every_steps = 100
      # Tuning happens at every epoch. The frequency can be increased here.
      config.tune_every_steps = 100_000
  
      # Data Pipeline Options
      config.prefetch_buffer_bytes = 16 * 1000 * 1000
      config.shuffle_buffer_elements = 10_000
      config.input_read_threads = 32
  
      # Placeholder value for limiting training examples. 0=No limit.
      config.limit = 0
  
      if config_name == "exome":
          config = get_exome_config(config)
      elif config_name == "base":
          # Use the base config.
          pass
      else:
          raise ValueError(f"Unknown config_name: {config_name}")
  
      return config
  
label_desc: Use image node_id 2 for calling a global function with example usage: get_config(config_name) and returns: config