code: def test_customize_model(self) -> None:
      @cflearn.MLModel.register("foo")
      class _(cflearn.MLModel):
          def __init__(self) -> None:
              super().__init__()
              self.dummy = torch.nn.Parameter(torch.tensor([1.0]))
  
          def forward(self, net: torch.Tensor) -> torch.Tensor:
              net = net.new_empty([net.shape[0], 1]).fill_(1.0)
              return {cflearn.PREDICTIONS_KEY: net}
  
      x = np.random.random([1000, 10])
      y = np.random.random([1000, 1])
      config = cflearn.MLConfig(
          model_name="foo", loss_name="mae", fixed_steps=0
      )
      m = cflearn.MLTrainingPipeline.init(config)
      data = cflearn.MLData.init().fit(x, y)
      m.fit(data)
      loader = m.data.build_loader(x)
      predictions = m.predict(loader)[cflearn.PREDICTIONS_KEY]
      self.assertTrue(np.allclose(predictions, np.ones_like(y)))
      params = list(m.build_model.model.parameters())[0]
      self.assertTrue(params is m.build_model.model.dummy)
  
imports: import torch
  import cflearn
  import unittest
  import numpy
label_desc: Use image node_id 1 for calling the TestCustomization obj's underlying member method code with example usage: obj.test_customize_model() and returns: dict