code: def test_slice(ctx):
      t1 = [torch.rand((2, 3, 4, 5)) for _ in range(3)]
      dt1 = DTensor.from_sharding_list(ctx, t1, num_partitions=3)
      assert torch.allclose(torch.slice_f(dt1, 3), t1[1][1])
  
      dt1 = DTensor.from_sharding_list(
          ctx, t1, num_partitions=3, axis=1
      )
      assert torch.slice_f(dt1, 1) == DTensor.from_sharding_list(
          ctx, [s[1] for s in t1], num_partitions=3
      )
  
      dt1 = DTensor.from_sharding_list(ctx, t1, num_partitions=3)
      # assert torch.allclose(torch.slice_f(dt1, [3,1,2]), torch.cat(t1)[[3,1,2]])
      print(torch.slice_f(dt1, [3, 1, 2]).shape)
      print(torch.cat(t1)[[3, 1, 2]].shape)
  
imports: import pytest
  import torch
  from fate.arch import Context
  from fate.arch.computing.backends.standalone import CSession
  from fate.arch.federation.backends.standalone import StandaloneFederation
  from fate.arch.tensor import DTensor
  from pytest import fixture
label_desc: Use image node_id 16 for calling a global function with example usage: test_slice(ctx) without return types