code: def test_gradient_norms_on_various_models(
      self,
      layer_name,
      param_tuple,
      num_microbatches,
      is_eager,
  ):
      # Parse inputs to generate test data. Note that each batched input is a
      # reshape of a `tf.range()` call.
      equation, input_dims, output_dims, bias_axes = param_tuple
      batch_size = 4
      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)
  
      # Make the layer generator via currying.
      einsum_generator = get_einsum_layer_generators()[layer_name]
  
      def curried_generator(a, b):
          del a, b
          return einsum_generator(equation, output_dims, bias_axes)
  
      # Load shared assets to all devices.
      with self.strategy.scope():
          model = common_test_utils.get_model_from_generator(
              model_generator=common_test_utils.make_one_layer_functional_model,
              layer_generator=curried_generator,
              input_dims=input_dims,
              output_dims=output_dims,
              is_eager=is_eager,
          )
  
      # Define the main testing ops. These may be later compiled to a Graph op.
      def test_op(x):
          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,
                  registry=get_einsum_layer_registry(),
              )
          )
  
      # TPUs can only run `tf.function`-decorated functions.
      if self.using_tpu:
          test_op = tf.function(test_op, autograph=False)
  
      # TPUs use lower precision than CPUs, so we relax our criterion.
      # E.g., one of the TPU runs generated the following results:
      #
      #   computed_norm = 93.48296
      #   true_norm     = 93.31176
      #   abs_diff      = 0.17120361
      #   rel_diff      = 0.00183475
      #
      # which is a reasonable level of error for computing gradient norms.
      # Other trials also give an absolute (resp. relative) error of around
      # 0.05 (resp. 0.0015).
      rtol = 1e-2 if self.using_tpu else 1e-3
      atol = 5e-1 if self.using_tpu else 1e-2
  
      # 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]
      expected_size = num_microbatches or batch_size
      self.assertEqual(tf.shape(computed_norms)[0], expected_size)
      self.assertAllClose(
          computed_norms, true_norms, rtol=rtol, atol=atol
      )
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/einsum_dense_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 einsum_dense
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(layer_name, param_tuple, num_microbatches, is_eager) and returns: einsum_generator, common_test_utils