code: def test_local_ranker(setup):
      y = (y_raw * 10).astype(mt.int32)
      ranker = LGBMRanker(n_estimators=2)
      ranker.fit(X_raw, y, group=[X_raw.shape[0]])
      prediction = ranker.predict(X_raw)
  
      assert prediction.ndim == 1
      assert prediction.shape[0] == len(X_raw)
  
      assert isinstance(prediction, mt.Tensor)
      result = prediction.fetch()
      assert prediction.dtype == result.dtype
  
      # test weight
      weight = mt.random.rand(X_raw.shape[0])
      ranker = LGBMRanker(verbosity=1, n_estimators=2)
      ranker.fit(X_raw, y, group=[X_raw.shape[0]], sample_weight=weight)
      prediction = ranker.predict(X_raw)
  
      assert prediction.ndim == 1
      assert prediction.shape[0] == len(X_raw)
      result = prediction.fetch()
      assert prediction.dtype == result.dtype
  
      # test local model
      X_np = X_raw.execute().fetch()
      y_np = y.execute().fetch()
      raw_ranker = lightgbm.LGBMRanker(verbosity=1, n_estimators=2)
      raw_ranker.fit(X_np, y_np, group=[X_raw.shape[0]])
      prediction = LGBMRanker(raw_ranker).predict(X_raw)
  
      assert prediction.ndim == 1
      assert prediction.shape[0] == len(X_raw)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_local_ranker(setup) without return types