code: def get_config():
      """Returns the base experiment configuration for ImageNet."""
      config = ml_collections.ConfigDict()
      config.experiment_name = "imagenet_bit_resnet"
      # Dataset.
      config.dataset_name = "imagenet"
      config.data_dtype_str = "float32"
      config.dataset_configs = ml_collections.ConfigDict()
  
      # Model.
      config.model_name = "bit_resnet_multilabel_classification"
      config.width_factor = 1
      config.num_layers = 50
      config.model_dtype_str = "float32"
  
      # Training.
      config.trainer_name = "classification_trainer"
      config.optimizer = "momentum_hp"
      config.optimizer_configs = ml_collections.ConfigDict()
      config.optimizer_configs.momentum = 0.9
      config.explicit_weight_decay = 1e-3
      config.l2_decay_factor = None
      config.max_grad_norm = None
      config.label_smoothing = None
      config.num_training_epochs = 90
      config.batch_size = 1024
      config.rng_seed = 0
      config.init_head_bias = -10.0
  
      # Learning rate.
      steps_per_epoch = _IMAGENET_TRAIN_SIZE // config.batch_size
      total_steps = config.num_training_epochs * steps_per_epoch
      base_lr = 0.1 * config.batch_size / 256
      # setting 'steps_per_cycle' to total_steps basically means non-cycling cosine.
      config.lr_configs = ml_collections.ConfigDict()
      config.lr_configs.learning_rate_schedule = "compound"
      config.lr_configs.factors = (
          "constant * cosine_decay * linear_warmup"
      )
      config.lr_configs.warmup_steps = 5000
      config.lr_configs.steps_per_cycle = total_steps
      config.lr_configs.base_learning_rate = base_lr
  
      # Logging.
      config.write_summary = True
      config.xprof = True  # Profile using xprof.
      config.checkpoint = True  # Do checkpointing.
      config.checkpoint_steps = 10 * steps_per_epoch
      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