code: def test_compile_dtensor_redistribute_backward(self):
      mesh = DeviceMesh(
          device_type="cuda", mesh=torch.arange(self.world_size)
      )
  
      def fn(x, y):
          dt = DTensor.from_local(
              x.reshape(2, 4), mesh, [Shard(0)], run_check=False
          )
          dt2 = DTensor.from_local(
              y.reshape(4, 2), mesh, [Shard(1)], run_check=False
          )
          dt_out = torch.matmul(dt, dt2)
          dt_out_redistribute = dt_out.redistribute(mesh, [Replicate()])
          return dt_out_redistribute.to_local()
  
      opt_fn = torch.compile(
          fn, backend=aot_eager_graph, fullgraph=True
      )
  
      x_ref = torch.arange(8, requires_grad=True, dtype=torch.float32)
      y_ref = torch.arange(8, requires_grad=True, dtype=torch.float32)
      ref = fn(x_ref, y_ref)
  
      x = torch.arange(8, requires_grad=True, dtype=torch.float32)
      y = torch.arange(8, requires_grad=True, dtype=torch.float32)
      res = opt_fn(x, y)
  
      self.assertEqual(res, ref)
  
      # Now run and assert the backward + gradients
      ref.sum().backward()
      res.sum().backward()
  
      self.assertEqual(x_ref.grad, x.grad)
      self.assertEqual(y_ref.grad, y.grad)
  
imports: import copy
  import functools
  import unittest
  from unittest.mock import patch
  import torch
  import torch._dynamo
  import torch.distributed
  import torch.nn
  from torch._C import FileCheck
  from torch._inductor.utils import run_and_get_triton_code
  from torch.distributed._tensor import DeviceMesh, DTensor, init_device_mesh, Replicate, Shard
  from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import checkpoint_wrapper, CheckpointImpl
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, PrepareModuleInput, PrepareModuleOutput, RowwiseParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, MLPModule, with_comms
  from torch.testing._internal.distributed.fake_pg import FakeStore
  from torch.utils._triton import has_triton
  from torch.utils.checkpoint import checkpoint
  from functorch.compile import min_cut_rematerialization_partition
  from torch._dynamo.backends.common import aot_autograd
label_desc: Use image node_id 5 for calling the TestDTensorCompileE2E obj's underlying member method code with example usage: obj.test_compile_dtensor_redistribute_backward() and returns: dt_out_redistribute