code: def testFunctionWithMultiMeshInputOutputs(self):
      self.skipForDeviceType(
          ["CPU"],
          "Skipping test as only CPU mesh is available for multi-meshtest.",
      )
  
      host_device_id = test_util.create_device_ids_array((1,))
      host_cpu_mesh = Mesh(
          [_MESH_DIM_X],
          host_device_id,
          np.ravel(host_device_id).tolist(),
          test_util.create_device_list((1,), "CPU"),
      )
      replicated_layout_on_cpu = Layout.replicated(
          host_cpu_mesh, rank=1
      )
      replicated_layout_on_cpu_r0 = Layout.replicated(
          host_cpu_mesh, rank=0
      )
      replicated_layout_on_tpu_r0 = Layout.replicated(
          self.second_mesh, rank=0
      )
      replicated_layout_on_tpu = Layout.replicated(
          self.second_mesh, rank=1
      )
  
      a = constant_op.constant([1, 2, 3, 4], dtype=dtypes.int32)
      b = constant_op.constant([1, 2, 3, 4], dtype=dtypes.int32)
  
      def golden_func(t1, t2):
          t1 = math_ops.cast(t1, dtypes.float32)
          t1 = t1 * t1
          t2 = math_ops.cast(t2, dtypes.float32)
          t2 = math_ops.reduce_sum(t2)
          out1 = gen_math_ops.neg(t2)
  
          t2 = t2 + t1
          out0 = math_ops.sqrt(t2)
          return out0, out1
  
      golden_result0, golden_result1 = golden_func(a, b)
  
      cpu_dtensor = api.copy_to_mesh(a, replicated_layout_on_cpu)
      tpu_dtensor = api.copy_to_mesh(b, replicated_layout_on_tpu)
  
      @polymorphic_function.function
      def cpu_func(t1, t2):
          t2 = t2 + t1
          return math_ops.sqrt(t2)
  
      @polymorphic_function.function
      def func(tpu_input, cpu_input):
          cpu_input = math_ops.cast(cpu_input, dtypes.float32)
          cpu_input = cpu_input * cpu_input
          tpu_input = math_ops.cast(tpu_input, dtypes.float32)
          tpu_input = math_ops.reduce_sum(tpu_input)
          tpu_output = gen_math_ops.neg(tpu_input)
  
          cpu_tensor = api.copy_to_mesh(
              tpu_input, replicated_layout_on_cpu_r0
          )
          cpu_output = cpu_func(cpu_tensor, cpu_input)
          return cpu_output, tpu_output
  
      with ops.device_v2(api.device_name()):
          output0, output1 = func(tpu_dtensor, cpu_dtensor)
  
      self.assertDTensorEqual(
          golden_result0, replicated_layout_on_cpu, output0
      )
      self.assertDTensorEqual(
          golden_result1, replicated_layout_on_tpu_r0, output1
      )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.dtensor.python import accelerator_util
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import d_variable
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  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.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 16 for calling the MultiMeshTest obj's underlying member method code with example usage: obj.testFunctionWithMultiMeshInputOutputs() and returns: out0,  out1, math_ops, cpu_output,  tpu_output