code: def _test_robust_scaler_floats(
      self, with_centering, with_scaling, backend="torch"
  ):
      # 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)
  
      model = RobustScaler(
          with_centering=with_centering, with_scaling=with_scaling
      )
      model.fit(data)
      torch_model = hummingbird.ml.convert(model, backend, data)
      self.assertIsNotNone(torch_model)
      np.testing.assert_allclose(
          model.transform(data),
          torch_model.transform(data_tensor),
          rtol=1e-06,
          atol=1e-06,
      )
  
imports: import unittest
  import numpy
  import torch
  from sklearn.preprocessing import RobustScaler, MaxAbsScaler, MinMaxScaler, StandardScaler
  import hummingbird.ml
  from hummingbird.ml._utils import tvm_installed
  from hummingbird.ml import constants
label_desc: Use image node_id 1 for calling the TestSklearnScalerConverter obj's underlying member method code with example usage: obj._test_robust_scaler_floats(with_centering, with_scaling, backend) without return types