code: def test_gradient_norms_on_various_models(
      self,
      model_name,
      layer_name,
      parameter_tuple,
      layer_registry_name,
      num_microbatches,
      is_eager,
  ):
      # Parse inputs to generate test data.
      input_dims, parameter_axes = parameter_tuple
  
      def curried_generator(a, b):
          del a, b  # Unused by the generator.
          layer_norm_generator = get_layer_norm_layer_generators()[
              layer_name
          ]
          return layer_norm_generator(parameter_axes)
  
      # Load shared assets to all devices.
      with self.strategy.scope():
          dummy_output_dim = 1
          model = common_test_utils.get_model_from_generator(
              model_generator=get_layer_norm_model_generators()[
                  model_name
              ],
              layer_generator=curried_generator,
              input_dims=input_dims,
              output_dims=[dummy_output_dim],
              is_eager=is_eager,
          )
  
      # Define the main testing ops. These may be later compiled to a Graph op.
      def test_op(x_batch):
          return (
              common_test_utils.get_computed_and_true_norms_from_model(
                  model=model,
                  per_example_loss_fn=None,
                  num_microbatches=num_microbatches,
                  x_batch=[x_batch, x_batch]
                  if model_name == "tower2"
                  else x_batch,
                  weight_batch=None,
                  registry=get_layer_norm_registries()[
                      layer_registry_name
                  ],
              )
          )
  
      # TPUs can only run `tf.function`-decorated functions.
      if self.using_tpu:
          test_op = tf.function(
              test_op, jit_compile=True, autograph=False
          )
  
      # TPUs use lower precision than CPUs, so we relax our criterion (see
      # `dense_test.py` for additional discussions).
      rtol = 1e-2 if self.using_tpu else 1e-3
      atol = 1e-1 if self.using_tpu else 1e-2
  
      # Each batched input is a reshape of a `tf.range()` call.
      batch_size = 2
      example_size = np.prod(input_dims)
      example_values = tf.range(
          batch_size * example_size, dtype=tf.float32
      )
      x_batch = tf.reshape(example_values, [batch_size] + input_dims)
      batch_size = x_batch.shape[0]
      # Set up the device ops and run the test.
      computed_norms, true_norms = self.strategy.run(
          test_op, args=(x_batch,)
      )
      # TPUs return replica contexts, which must be unwrapped.
      if self.using_tpu:
          common_test_utils.assert_replica_values_are_close(
              self, computed_norms
          )
          common_test_utils.assert_replica_values_are_close(
              self, true_norms
          )
          computed_norms = computed_norms.values[0]
          true_norms = true_norms.values[0]
      self.assertEqual(tf.shape(computed_norms)[0], batch_size)
      self.assertAllClose(
          computed_norms, true_norms, rtol=rtol, atol=atol
      )
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import common_test_utils
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import dense
  from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import layer_normalization
label_desc: Use image node_id 2 for calling the GradNormTest obj's underlying member method code with example usage: obj.test_gradient_norms_on_various_models(model_name, layer_name, parameter_tuple, layer_registry_name, num_microbatches, is_eager) and returns: layer_norm_generator, common_test_utils