code: def test_lightgbm_tvm_classifier_large_dataset(self):
      warnings.filterwarnings("ignore")
  
      for tree_implementation in [
          "gemm",
          "tree_trav",
          "perf_tree_trav",
      ]:
          size = 200000
          X = np.random.rand(size, 28)
          X = np.array(X, dtype=np.float32)
          y = np.random.randint(2, size=size)
          model = lgb.LGBMClassifier(n_estimators=100, max_depth=3)
          model.fit(X, y)
  
          # Create TVM model.
          tvm_model = hummingbird.ml.convert(
              model,
              "tvm",
              X,
              extra_config={
                  constants.TREE_IMPLEMENTATION: tree_implementation,
                  constants.TREE_OP_PRECISION_DTYPE: "float64",
              },
          )
  
          # Check results.
          np.testing.assert_allclose(
              tvm_model.predict_proba(X),
              model.predict_proba(X),
              rtol=1e-04,
              atol=1e-04,
          )
  
imports: import unittest
  import warnings
  import numpy
  import hummingbird.ml
  from hummingbird.ml import constants
  from hummingbird.ml._utils import lightgbm_installed, onnx_runtime_installed, tvm_installed
  from tree_utils import gbdt_implementation_map
label_desc: Use image node_id 45 for calling the TestLGBMConverter obj's underlying member method code with example usage: obj.test_lightgbm_tvm_classifier_large_dataset() without return types