code: def test_checkpoint_in_a_train_loop(self, shard_dims):
      # This test is a parallel test with save_restore_test's
      # DTensorSaveRestoreTest.test_checkpoint
  
      class M(module.Module):
          # Pass in both replicated and sharded for better coverage.
          def __init__(self, replicated_value, sharded_value):
              # This is actually a DVariable.
              self.r = d_variable.DVariable(replicated_value)
              self.s = d_variable.DVariable(sharded_value)
  
          def __call__(self, x):
              return math_ops.reduce_sum(
                  x + self.r
              ) + math_ops.reduce_sum(x + self.s)
  
      directory = self.get_temp_dir()
  
      sharded_np = np.arange(8).reshape((2, 4)).astype(np.float32)
      replicated_np = np.arange(16).reshape((8, 2)).astype(np.float32)
  
      replicated_layout = Layout.replicated(self.mesh, rank=2)
      one_d_sharded_layout = Layout(shard_dims, self.mesh)
  
      replicated_value = api.copy_to_mesh(
          replicated_np, replicated_layout
      )
      replicated_zeros = api.copy_to_mesh(
          np.zeros((8, 2)).astype(np.float32), replicated_layout
      )
  
      sharded_value = numpy_util.pack_numpy(
          sharded_np, one_d_sharded_layout
      )
      sharded_zeros = numpy_util.pack_numpy(
          np.zeros((2, 4)).astype(np.float32), one_d_sharded_layout
      )
  
      # Training loop that just increments the model's variable every "epoch"
      # to test checkpointing.
      for epoch in range(5):
          m = M(replicated_value, sharded_value)
  
          ckpt = checkpoint.Checkpoint(model=m)
          manager = checkpoint_management.CheckpointManager(
              ckpt, directory=directory, max_to_keep=None
          )
  
          ckpt.restore(manager.latest_checkpoint)
  
          # Ensure that the variable is created
          m(api.copy_to_mesh(1.0, Layout.replicated(self.mesh, rank=0)))
  
          self.assertDTensorEqual(
              epoch + replicated_np, replicated_layout, m.r
          )
          self.assertDTensorEqual(
              epoch + sharded_np, one_d_sharded_layout, m.s
          )
  
          m.s.assign_add(
              numpy_util.pack_numpy(
                  np.ones((2, 4), dtype=np.float32),
                  one_d_sharded_layout,
              )
          )
          m.r.assign_add(
              api.copy_to_mesh(
                  constant_op.constant(
                      np.ones((8, 2), dtype=np.float32)
                  ),
                  replicated_layout,
              )
          )
  
          checkpoint_number = epoch + 1
  
          stats1 = api._dtensor_device()._get_stats()
          manager.save(checkpoint_number=checkpoint_number)
  
          gc.collect()
          stats2 = api._dtensor_device()._get_stats()
          keys = set(stats2.keys())
          keys.update(stats1.keys())
          diff = {k: stats2.get(k, 0) - stats1.get(k, 0) for k in keys}
          diff = {k: v for k, v in diff.items() if v != 0}
  
          m.s.assign(sharded_zeros)
          m.r.assign(replicated_zeros)
  
imports: import gc
  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.checkpoint import checkpoint
  from tensorflow.python.checkpoint import checkpoint_management
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.module import module
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 6 for calling the DTensorSaveRestoreV2Test obj's underlying member method code with example usage: obj.test_checkpoint_in_a_train_loop(shard_dims) and returns: unknown