code: def test_strided_slice_grad(self):
      np.random.seed(0)
      random_initial_value = np.random.uniform(size=4).reshape([4])
      random_initial_value = numpy_util.pack_numpy(
          random_initial_value, self.unsharded_layout
      )
  
      @polymorphic_function.function
      def fn_with_strided_slice(t):
          a = array_ops.strided_slice(t, [1], [2], shrink_axis_mask=1)
          return math_ops.sqrt(a)
  
      random_variable = d_variable.DVariable(random_initial_value)
      with backprop.GradientTape() as tape:
          output = fn_with_strided_slice(random_variable)
  
      grads = tape.gradient(output, [random_variable])
      self.assertTrue(api.fetch_layout(grads[0]).is_fully_replicated())
  
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 12 for calling the LayoutPropagationV2Test obj's underlying member method code with example usage: obj.test_strided_slice_grad() and returns: math_ops