code: def test_normalizer_converter(self):
      # Generate a random 2D array with values in [0, 1000)
      np.random.seed(0)
      data = np.random.rand(100, 200) * 1000
      data = np.array(data, dtype=np.float32)
      data_tensor = torch.from_numpy(data)
  
      for norm in ["l1", "l2", "max"]:
          model = Normalizer(norm=norm)
          model.fit(data)
  
          torch_model = hummingbird.ml.convert(model, "torch")
  
          self.assertIsNotNone(torch_model)
          np.testing.assert_allclose(
              model.transform(data),
              torch_model.transform(data_tensor),
              rtol=1e-06,
              atol=1e-06,
          )
  
file_path: hummingbird/tests/test_sklearn_normalizer_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.preprocessing import Normalizer
  import hummingbird.ml
  from hummingbird.ml import constants
  from hummingbird.ml._utils import onnx_runtime_installed, tvm_installed
label_desc: Use image node_id 1 for calling the TestSklearnNormalizer obj's underlying member method code with example usage: obj.test_normalizer_converter() without return types