code: def testNestedFunction(self, distribution, model_in_tf_function):
      def model(x):
          return x * x
  
      if model_in_tf_function:
          model = def_function.function(model)
  
      with distribution.scope():
          x = variables.Variable(1.0)
  
          @def_function.function
          def train_step():
              def replica_step():
                  with backprop.GradientTape() as tape:
                      y = model(x)
                  return tape.gradient(y, x)
  
              return distribution.run(replica_step)
  
          grads = distribution.experimental_local_results(train_step())
          self.assertLen(grads, distribution.num_replicas_in_sync)
          self.assertTrue(all(g is not None for g in grads))
  
imports: from absl.testing import parameterized
  from tensorflow.python import tf2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
label_desc: Use image node_id 3 for calling the GradientTapeTest obj's underlying member method code with example usage: obj.testNestedFunction(distribution, model_in_tf_function) and returns: unknown, distribution, tape