docstr: Tests that DPSequential works when DP-SGD has no effect.
docstr_tok: ["Tests","that","DPSequential","works","when","DP-SGD","has","no","effect","."]
code: def testBaseline(self):
      """Tests that DPSequential works when DP-SGD has no effect."""
      train_data, train_labels = get_data()
  
      # Simple linear model returns w * x + b.
      model = dp_keras_model.DPSequential(
          l2_norm_clip=1.0e9,
          noise_multiplier=0.0,
          layers=[
              tf.keras.layers.InputLayer(input_shape=(2,)),
              tf.keras.layers.Dense(
                  1,
                  kernel_initializer="zeros",
                  bias_initializer="zeros",
              ),
          ],
      )
      optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
      loss = tf.keras.losses.MeanSquaredError()
  
      model.compile(optimizer=optimizer, loss=loss)
      model.fit(train_data, train_labels, epochs=1, batch_size=1)
  
      model_weights = model.get_weights()
  
      # Check parameters are as expected, taking into account the learning rate.
      self.assertAllClose(model_weights[0], [[0.90], [1.20]])
      self.assertAllClose(model_weights[1], [0.30])
  
file_path: privacy/tensorflow_privacy/privacy/keras_models/dp_keras_model_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.keras_models import dp_keras_model
label_desc: Use image node_id 1 for calling the DPKerasModelTest obj's underlying member method code with example usage: obj.testBaseline() without return types