code: def test_training_works(
      self, strategy, clip_norm, model_or_sequential, fast_clipping
  ):
      if model_or_sequential == "sequential" and fast_clipping:
          self.skipTest("Fast clipping does not work for DPSequential.")
  
      if isinstance(strategy, tf.distribute.OneDeviceStrategy):
          strategy_scope = contextlib.nullcontext()
      else:
          strategy_scope = strategy.scope()
  
      n = 10
      x, y, expected_grad = get_data(n, clip_norm)
  
      def make_model():
          dense_layer = tf.keras.layers.Dense(
              1, kernel_initializer="zeros", bias_initializer="zeros"
          )
          dp_kwargs = dict(
              l2_norm_clip=clip_norm,
              noise_multiplier=0.0,
              num_microbatches=None,
              use_xla=False,
              layer_registry=layer_registry.make_default_layer_registry()
              if fast_clipping
              else None,
          )
          if model_or_sequential == "sequential":
              model = dp_keras_model.DPSequential(
                  layers=[dense_layer],
                  **dp_kwargs,
              )
          else:
              inputs = tf.keras.layers.Input((2,))
              outputs = dense_layer(inputs)
              model = dp_keras_model.DPModel(
                  inputs=inputs, outputs=outputs, **dp_kwargs
              )
          return model
  
      with strategy_scope:
          model = make_model()
          self.assertEqual(
              model._enable_fast_peg_computation, fast_clipping
          )
          lr = 0.01
          opt = tf.keras.optimizers.SGD(learning_rate=lr)
          loss = tf.keras.losses.MeanSquaredError(
              reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
          )
          model.compile(loss=loss, optimizer=opt)
          history = model.fit(
              x=x,
              y=y,
              epochs=1,
              batch_size=x.shape[0],
              steps_per_epoch=1,
          )
          self.assertIn("loss", history.history)
          self.assertEqual(opt.iterations.numpy(), 1)
          model_weights = model.get_weights()
  
          expected_val = -expected_grad * lr
          self.assertAllClose(
              model_weights[0], expected_val[:2].reshape(-1, 1)
          )
          self.assertAllClose(model_weights[1], [expected_val[2]])
  
file_path: privacy/tensorflow_privacy/privacy/keras_models/dp_keras_model_distributed_test.py
imports: import contextlib
  import multiprocessing
  import os
  import sys
  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 DPKerasModelDistributedTest obj's underlying member method code with example usage: obj.test_training_works(strategy, clip_norm, model_or_sequential, fast_clipping) and returns: model