code: def get_config():
      """Get the default hyperparameter configuration."""
      config = ml_collections.ConfigDict()
  
      # As defined in the `models` module.
      config.model = "ResNet50"
      # `name` argument of tensorflow_datasets.builder()
      config.dataset = "imagenet2012:5.*.*"
  
      config.learning_rate = 0.1
      config.warmup_epochs = 5.0
      config.momentum = 0.9
      config.batch_size = 128
      config.shuffle_buffer_size = 16 * 128
      config.prefetch = 10
  
      config.num_epochs = 100.0
      config.log_every_steps = 100
  
      config.cache = False
      config.half_precision = False
  
      # If num_train_steps==-1 then the number of training steps is calculated from
      # num_epochs using the entire dataset. Similarly for steps_per_eval.
      config.num_train_steps = -1
      config.steps_per_eval = -1
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config() and returns: config