code: def testRunInFunctionGradient(self, distribution):
      dataset = get_dataset_from_tensor_slices(
          [5.0, 6.0, 7.0, 8.0]
      ).batch(2)
  
      @def_function.function
      def run(x):
          def train_step(x):
              def computation(x):
                  return math_ops.square(x)
  
              with backprop.GradientTape() as tape:
                  tape.watch(x)  # Manually watch non-variable tensors.
                  y = computation(x)
              grads = tape.gradient(y, x)
              return grads
  
          return distribution.experimental_local_results(
              distribution.run(train_step, args=(x,))
          )
  
      dist_dataset = distribution.experimental_distribute_dataset(
          dataset
      )
      results = []
      for x in dist_dataset:
          output = run(x)
          results.append(output)
      self.assert_equal_flattened([[10.0, 12.0], [14.0, 16.0]], results)
  
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 2 for calling the GradientTapeTest obj's underlying member method code with example usage: obj.testRunInFunctionGradient(distribution) and returns: distribution, grads, math_ops