code: def get_torchbiggraph_config():
      config = dict(  # noqa
          # I/O data
          entity_path="data/FB15k",
          edge_paths=[
              "data/FB15k/freebase_mtr100_mte100-train_partitioned",
              "data/FB15k/freebase_mtr100_mte100-valid_partitioned",
              "data/FB15k/freebase_mtr100_mte100-test_partitioned",
          ],
          checkpoint_path="model/fb15k",
          # Graph structure
          entities={"all": {"num_partitions": 1}},
          relations=[
              {
                  "name": "all_edges",
                  "lhs": "all",
                  "rhs": "all",
                  "operator": "complex_diagonal",
              }
          ],
          dynamic_relations=True,
          # Scoring model
          dimension=400,
          global_emb=False,
          comparator="dot",
          # Training
          num_epochs=50,
          batch_size=5000,
          num_uniform_negs=1000,
          loss_fn="softmax",
          lr=0.1,
          regularization_coef=1e-3,
          # Evaluation during training
          eval_fraction=0,
          # GPU
          num_gpus=1,
      )
  
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_torchbiggraph_config() and returns: config