code: def test_while_microbatch_with_reused_gradient_accumulator(
      self, sharded_layout
  ):
      layouts = [
          self.unsharded_unsharded_layout,
          self.x_unsharded_layout,
          self.unsharded_x_layout,
      ]
      sharded_layout_0 = layouts[sharded_layout]
      sharded_layout_1 = layouts[2]
  
      np.random.seed(0)
      random_initial_value_1 = (
          np.random.uniform(size=4 * 4)
          .reshape([4, 4])
          .astype(np.float32)
      )
      random_initial_value_2 = (
          np.random.uniform(size=4 * 4)
          .reshape([4, 4])
          .astype(np.float32)
      )
      random_batch = (
          np.random.uniform(size=12 * 4)
          .reshape([12, 4])
          .astype(np.float32)
      )
  
      golden_variable_1 = variables.Variable(random_initial_value_1)
      golden_variable_2 = variables.Variable(random_initial_value_2)
  
      @polymorphic_function.function
      def update_weights(batch, variable1, variable2):
          accum_grads = array_ops.zeros_like_v2(variable1)
          accum_grads_2 = array_ops.zeros_like_v2(variable2)
  
          for i in math_ops.range(3):
              reshaped = array_ops.reshape(batch, [4, 3, 4])
              mini_batch = array_ops.gather_v2(reshaped, i, axis=1)
  
              with backprop.GradientTape() as tape:
                  logits_1 = variable1 * variable1 + mini_batch
                  logits_2 = variable2 * variable2 + mini_batch
                  loss_1 = math_ops.reduce_sum(logits_1 * logits_1)
                  loss_2 = math_ops.reduce_sum(logits_2 * logits_2)
                  loss = loss_1 + loss_2
              grads = tape.gradient(loss, [variable1, variable2])
              accum_grads += grads[0]
              accum_grads_2 += grads[1]
  
          new_variable = variable1 + accum_grads
          new_variable_2 = variable2 + accum_grads_2
          variable1.assign(new_variable)
          variable2.assign(new_variable_2)
          return accum_grads, accum_grads_2
  
      golden_accum, golden_accum_2 = update_weights(
          constant_op.constant(random_batch),
          golden_variable_1,
          golden_variable_2,
      )
  
      random_batch = numpy_util.pack_numpy(
          random_batch, sharded_layout_0
      )
      random_initial_value_1 = numpy_util.pack_numpy(
          random_initial_value_1, sharded_layout_0
      )
      dtensor_variable_1 = d_variable.DVariable(random_initial_value_1)
  
      random_initial_value_2 = numpy_util.pack_numpy(
          random_initial_value_2, sharded_layout_1
      )
      dtensor_variable_2 = d_variable.DVariable(random_initial_value_2)
  
      dtensor_accum, dtensor_accum_2 = update_weights(
          random_batch, dtensor_variable_1, dtensor_variable_2
      )
      self.assertDTensorEqual(
          golden_accum, sharded_layout_0, dtensor_accum
      )
      self.assertDTensorEqual(
          golden_accum_2, sharded_layout_1, dtensor_accum_2
      )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import d_variable
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import numpy_util
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the LayoutPropagationV2Test obj's underlying member method code with example usage: obj.test_while_microbatch_with_reused_gradient_accumulator(sharded_layout) and returns: accum_grads,  accum_grads_2