code: def test_evaluate_without_filter():
      dataset = load_fb15k_237()
  
      # create the model with transe scoring function
      model = ScoringBasedEmbeddingModel(
          eta=5, k=300, scoring_type="TransE"
      )
  
      # compile the model with loss and optimizer
      model.compile(optimizer="adam", loss="multiclass_nll")
  
      # fit the model to data.
      model.fit(dataset["train"], batch_size=10000, epochs=10)
  
      print("----------EVAL WITHOUT FILTER-----------------")
      print(
          "----------corrupted with default protocol-----------------"
      )
      # evaluate on the test set
      ranks = model.evaluate(
          dataset["test"][::100],  # test set
          batch_size=100,  # evaluation batch size
          corrupt_side="s,o",  # sides to corrupt for scoring and ranking
      )
  
      mr_joint = mr_score(ranks)
      mrr_joint = mrr_score(ranks)
      print("MR:", mr_joint)
      print("MRR:", mrr_joint)
      print("hits@1:", hits_at_n_score(ranks, 1))
      print("hits@10:", hits_at_n_score(ranks, 10))
  
      print(
          "----------Subj and obj corrupted separately-----------------"
      )
      ranks_sep = []
      ranks1 = model.evaluate(
          dataset["test"][::100],  # test set
          batch_size=100,  # evaluation batch size
          corrupt_side="s",  # sides to corrupt for scoring and ranking
      )
      ranks_sep.extend(ranks1)
  
      ranks2 = model.evaluate(
          dataset["test"][::100],  # test set
          batch_size=100,  # evaluation batch size
          corrupt_side="o",  # sides to corrupt for scoring and ranking
      )
      ranks_sep.extend(ranks2)
      mr_sep = mr_score(ranks_sep)
      print("MR:", mr_sep)
      print("MRR:", mrr_score(ranks_sep))
      print("hits@1:", hits_at_n_score(ranks_sep, 1))
      print("hits@10:", hits_at_n_score(ranks_sep, 10))
  
      np.testing.assert_equal(mr_sep, mr_joint)
      assert mrr_joint is not np.Inf, "MRR is infinity"
  
imports: import ampligraph
  from ampligraph.datasets import load_fb15k_237
  from ampligraph.latent_features import ScoringBasedEmbeddingModel
  from ampligraph.evaluation.metrics import mrr_score, hits_at_n_score, mr_score
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_evaluate_without_filter() without return types