code: def testRecomputeGrad(self):
      graph = mtf.Graph()
      mesh = mtf.Mesh(graph, "my_mesh")
  
      # let's differentiate x^2 + x
      # dy/dx = 2x+1
      def x_squared_plus_x(x):
          return x * x + x
  
      x = tf.constant([5, 10], dtype=tf.float32)
      dy = tf.constant([2, 3], dtype=tf.float32)
      two = mtf.Dimension("two", 2)
      expected_y = tf.constant([30, 110], dtype=tf.float32)
      expected_dx = tf.constant([22, 63], dtype=tf.float32)
      mtf_x = mtf.import_fully_replicated(
          mesh, x, shape=mtf.Shape([two])
      )
      mtf_dy = mtf.import_tf_tensor(mesh, dy, shape=mtf.Shape([two]))
      mtf_y = mtf.recompute_grad(x_squared_plus_x, [mtf_x])
      [mtf_dx] = mtf.gradients([mtf_y], [mtf_x], [mtf_dy])
      mesh_impl = mtf.placement_mesh_impl.PlacementMeshImpl(
          shape="processors:2",
          layout="two:processors",
          devices=["", ""],
      )
      lowering = mtf.Lowering(graph, {mesh: mesh_impl})
      actual_y = lowering.export_to_tf_tensor(mtf_y)
      actual_dx = lowering.export_to_tf_tensor(mtf_dx)
      self.assertAllEqual(
          self.evaluate(actual_y), self.evaluate(expected_y)
      )
      self.assertAllEqual(
          self.evaluate(actual_dx), self.evaluate(expected_dx)
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import mesh_tensorflow
  from mesh_tensorflow import test_utils
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 1 for calling the RecomputeGradTest obj's underlying member method code with example usage: obj.testRecomputeGrad() and returns: unknown