code: def get_hyper_params():
      """Parameters that currently can be tuned during HPO"""
      hyper_params = {
          "lr": 3.6e-3,  # Learning rate
          # Options: Real(5e-5, 5e-3)
          "weight_decay": 1e-6,  # Rate of linear weight decay for learning rate
          # Options: Real(1e-6, 5e-2)
          "p_dropout": 0,  # dropout probability, 0 turns off Dropout.
          # Options: Categorical(0, 0.1, 0.2, 0.3, 0.4, 0.5)
          "n_heads": 4,  # Number of attention heads
          # Options: Categorical(2, 4, 8)
          "hidden_dim": 128,  # hidden dimension size
          # Options: Categorical(32, 64, 128, 256)
          "n_layers": 2,  # Number of Tab Transformer encoder layers,
          # Options: Categorical(1, 2, 3, 4, 5)
          "feature_dim": 64,  # Size of fully connected layer in TabNet.
          # Options: Int(8, 128)
          "num_output_layers": 1  # How many fully-connected layers on top of transformer to produce predictions. Minimum 1 layer.
          # Options: Categorical(1, 2, 3)
      }
  
      return hyper_params
  
label_desc: Use image node_id 2 for calling a global function with example usage: get_hyper_params() and returns: hyper_params