code: def get_config():
      """Returns the X-ViT experiment configuration for metaphase sexID."""
      config = ml_collections.ConfigDict()
      config.experiment_name = "meta-sexid-xvit"
  
      # Dataset.
      config.dataset_name = "metaphase_sexid"
      config.data_dtype_str = "float32"
  
      # Model.
      config.model_name = "xvit_multilabel_classification"
      config.model_dtype_str = "float32"
      config.model = ml_collections.ConfigDict()
      config.model.patches = ml_collections.ConfigDict()
      config.model.hidden_size = 768
      config.model.patches = ml_collections.ConfigDict()
      config.model.patches.size = [4, 4]
      config.model.mlp_dim = 2048
      config.model.num_layers = 12
      config.model.representation_size = None
      config.model.classifier = "token"
      config.model.attention_dropout_rate = 0.0
      config.model.dropout_rate = 0.0
      config.model.transformer_encoder_configs = (
          ml_collections.ConfigDict()
      )
      config.model.transformer_encoder_configs.type = "global"
      config.model.attention_fn = "standard"
      config.model.attention_configs = ml_collections.ConfigDict()
      config.model.attention_configs.num_heads = 12
  
      # Training.
      config.trainer_name = "classification_trainer"
      config.optimizer = "adam"
      config.optimizer_configs = ml_collections.ConfigDict()
      config.optimizer_configs.beta1 = 0.9
      config.optimizer_configs.beta2 = 0.999
      config.optimizer_configs.weight_decay = 0.1
      config.explicit_weight_decay = None  # No explicit weight decay
      config.l2_decay_factor = None
      config.max_grad_norm = None
      config.label_smoothing = None
      config.num_training_epochs = 100
      config.log_eval_steps = 1000
      config.batch_size = 1024
      config.rng_seed = 42
      config.init_head_bias = -10.0
  
      # Learning rate.
      steps_per_epoch = _TRAIN_SIZE // config.batch_size
      total_steps = config.num_training_epochs * steps_per_epoch
      base_lr = 8e-4
      config.lr_configs = ml_collections.ConfigDict()
      config.lr_configs.learning_rate_schedule = "compound"
      config.lr_configs.factors = "constant*linear_warmup*linear_decay"
      config.lr_configs.total_steps = total_steps
      config.lr_configs.end_learning_rate = 1e-5
      config.lr_configs.warmup_steps = 1000
      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 = 5000
      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