code: def test_onnx_linear_converter_raises_rt(self):
      n_features = 20
      n_total = 100
      np.random.seed(0)
      warnings.filterwarnings("ignore")
      X = np.random.rand(n_total, n_features)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(3, size=n_total)
      model = LinearRegression()
      model.fit(X, y)
  
      # generate test input
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[
              ("float_input", FloatTensorType_onnx(X.shape))
          ],
      )
      onnx_ml_model.graph.node[0].attribute[0].name = "".encode()
  
      self.assertRaises(RuntimeError, convert, onnx_ml_model, "onnx", X)
  
file_path: hummingbird/tests/test_onnxml_linear_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier, LogisticRegressionCV
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed, lightgbm_installed
  from hummingbird.ml import convert
label_desc: Use image node_id 7 for calling the TestONNXLinear obj's underlying member method code with example usage: obj.test_onnx_linear_converter_raises_rt() without return types