callbacks:
  patience: 100
  tol: 0
data:
  baseMVA: 100
  mask_value: 0.0
  networks:
  - sundon_33kv
  normalization: HeteroDataMVANormalizer
  scenarios:
  - {N_SCENARIOS}
  split_by_load_scenario_idx: false
  test_ratio: 1.0
  val_ratio: 0.0
  workers: 4
  split_from_existing_files: {SPLIT_DIR}
model:
  attention_head: 8
  edge_dim: 10
  hidden_size: 48
  input_bus_dim: 15
  input_gen_dim: 6
  num_layers: 12
  output_bus_dim: 2
  output_gen_dim: 1
  type: GNS_heterogeneous
optimizer:
  beta1: 0.9
  beta2: 0.999
  learning_rate: 0.0005
  lr_decay: 0.7
  lr_patience: 5
seed: 0
task:
  task_name: PowerFlow
training:
  accelerator: auto
  batch_size: 64
  devices: auto
  epochs: 200
  loss_args:
  - base_weight: 0.5
  - {}
  loss_weights:
  - 0.1
  - 0.9
  losses:
  - LayeredWeightedPhysics
  - MaskedBusMSE
  strategy: auto
verbose: true