code: def test_gradient_norms_on_various_models(
      self,
      input_data,
      model_name,
      layer_registry_name,
      num_microbatches,
      is_eager,
  ):
      # Parse inputs to generate test data.
      input_dims, max_length, seq_axis = input_data
  
      # Load shared assets to all devices.
      with self.strategy.scope():
  
          def embed_layer_generator(a, b):
              del a, b  # Unused input variables.
              return tfm.nlp.layers.PositionEmbedding(
                  max_length=max_length,
                  seq_axis=seq_axis,
              )
  
          batch_size = 6
          dummy_output_dims = [1]
          example_size = tf.reduce_prod(input_dims)
          example_values = tf.range(
              batch_size * example_size, dtype=tf.float32
          )
          x_batch = tf.reshape(
              example_values, [batch_size] + input_dims
          )
          model = common_test_utils.get_model_from_generator(
              model_generator=(
                  get_nlp_position_embedding_model_generators()[
                      model_name
                  ]
              ),
              layer_generator=embed_layer_generator,
              input_dims=input_dims,
              output_dims=dummy_output_dims,
              is_eager=is_eager,
          )
  
      # Define the main testing ops. These may be later compiled to a Graph op.
      def test_op():
          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,
                  registry=(
                      get_nlp_position_embedding_layer_registries()[
                          layer_registry_name
                      ]
                  ),
                  partial=None,
              )
          )
  
      # TPUs can only run `tf.function`-decorated functions.
      if self.using_tpu:
          test_op = tf.function(test_op, autograph=False)
  
      # Set up the device ops and run the test.
      computed_norms, true_norms = self.strategy.run(test_op)
      # 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]
      expected_size = num_microbatches or batch_size
      self.assertEqual(tf.shape(computed_norms)[0], expected_size)
      self.assertAllClose(
          computed_norms, true_norms, rtol=1e-3, atol=1e-2
      )
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/nlp_position_embedding_test.py
imports: from absl.testing import parameterized
  import tensorflow
  import tensorflow_models
  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 nlp_position_embedding
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(input_data, model_name, layer_registry_name, num_microbatches, is_eager) and returns: common_test_utils, tfm