code: def get_config(
      data_name="rico", shuffle_buffer_size=10_000, use_inner=True
  ):
      """Returns configs for a datset given its name."""
      dataset_configs = ml_collections.ConfigDict()
      dataset_configs.shuffle_buffer_size = shuffle_buffer_size
      dataset_configs.prefetch_to_device = 5
      dataset_configs.use_inner = use_inner
  
      if data_name == "rico":
          # Add path to training files containing tf.Example.
          dataset_configs.train_files = ["/path/to/train_tfexample"]
          # Add path to eval (validation) files containing tf.Example.
          dataset_configs.eval_files = ["/path/to/eval_tfexample"]
  
      dataset_configs.dataset_name = data_name
      dataset_configs.task_name = "layout_denoise"
      dataset_configs.num_train_examples = TRAIN_SIZE[data_name]
      dataset_configs.num_eval_examples = EVAL_SIZE[data_name]
      dataset_configs.data_dtype_str = "float32"
      return dataset_configs
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config(data_name, shuffle_buffer_size, use_inner) and returns: dataset_configs