code: def test_while_microbatch(self, sharded_layout, use_split):
      layouts = [
          self.unsharded_unsharded_layout,
          self.x_unsharded_layout,
      ]
      sharded_layout = layouts[sharded_layout]
  
      np.random.seed(0)
      random_initial_value = np.random.uniform(size=4 * 4).reshape(
          [4, 4]
      )
      if use_split:
          random_batch = np.random.uniform(size=12 * 4).reshape([12, 4])
      else:
          random_batch = np.random.uniform(size=4 * 4).reshape([4, 4])
      golden_variable = variables.Variable(random_initial_value)
  
      @polymorphic_function.function
      def update_weights(batch, variable):
          accum_grads = array_ops.zeros_like_v2(variable)
          for i in math_ops.range(3):
              if use_split:
                  reshaped = array_ops.reshape(batch, [4, 3, 4])
                  mini_batch = array_ops.gather_v2(reshaped, i, axis=1)
              else:
                  mini_batch = batch
              with backprop.GradientTape() as tape:
                  logits = variable * variable + mini_batch
                  loss = math_ops.reduce_sum(logits * logits)
              accum_grads += tape.gradient(loss, variable)
          new_variable = variable + accum_grads
          variable.assign(new_variable)
          return accum_grads
  
      golden_accum = update_weights(
          constant_op.constant(random_batch), golden_variable
      )
  
      random_batch = numpy_util.pack_numpy(random_batch, sharded_layout)
      random_initial_value = numpy_util.pack_numpy(
          random_initial_value, sharded_layout
      )
      dtensor_variable = d_variable.DVariable(random_initial_value)
  
      dtensor_accum = update_weights(random_batch, dtensor_variable)
      self.assertDTensorEqual(
          golden_accum, sharded_layout, dtensor_accum
      )
  
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 4 for calling the LayoutPropagationV2Test obj's underlying member method code with example usage: obj.test_while_microbatch(sharded_layout, use_split) and returns: accum_grads