code: def test_index(self):
      meshes = [
          DeviceMesh(
              self.device_type, list(range(self.world_size))
          ),  # 1D mesh
          # TODO(@azzolini): un-comment when DTensorConverter supports N-D mesh
          # DeviceMesh(self.device_type, torch.arange(self.world_size).reshape(2, -1)), # 2D mesh
      ]
      for mesh in meshes:
          self._test_op(
              mesh,
              lambda x, y: x[y],
              torch.randn(16, 32, 16),
              torch.randint(5, (4, 8)),
          )
          self._test_op(
              mesh,
              lambda x, y: x.index_select(1, y),
              torch.randn(16, 32, 16),
              torch.randint(5, (4,)),
          )
          self._test_op(
              mesh,
              lambda x, y: x.index_select(0, y),
              torch.randn(16, 32, 16),
              torch.randint(5, (4,)),
          )
          self._test_op(
              mesh,
              lambda x, y: x[y],
              torch.randn(16, 32, 16),
              torch.randint(5, (12,)),
          )
          self._test_op(
              mesh,
              lambda x, y: x[:, y],
              torch.randn(16, 32, 16),
              torch.randint(5, (4, 8)),
          )
          self._test_op(
              mesh,
              lambda x, y: x[..., y],
              torch.randn(16, 32, 16),
              torch.randint(5, (4, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y: x[..., y],
              torch.randn(16, 32, 16),
              torch.randint(5, (4, 8, 16)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[z, y],
              torch.randn(16, 32, 16),
              torch.randint(5, (12, 8, 12)),
              torch.randint(2, (12, 8, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[z, :, y],
              torch.randn(16, 32, 16),
              torch.randint(5, (12, 8, 12)),
              torch.randint(2, (12, 8, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[:, z, :, y],
              torch.randn(16, 32, 16, 12),
              torch.randint(5, (12, 8, 12)),
              torch.randint(2, (12, 8, 12)),
          )
          # broadcast in inner dimensions
          self._test_op(
              mesh,
              lambda x, y, z: x[:, z, :, y],
              torch.randn(16, 32, 16, 12),
              torch.randint(5, (12, 8, 12)),
              torch.randint(2, (12, 1, 12)),
          )
          # implicit (left-padded) broadcast
          self._test_op(
              mesh,
              lambda x, y, z: x[:, z, :, y],
              torch.randn(16, 32, 16, 12),
              torch.randint(5, (12, 8, 12)),
              torch.randint(2, (8, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[z, y, :, :],
              torch.randn(16, 32, 16, 12),
              torch.randint(2, (8, 12)),
              torch.randint(5, (12, 8, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[z, :, y, :],
              torch.randn(16, 32, 16, 12),
              torch.randint(2, (8, 12)),
              torch.randint(5, (12, 8, 12)),
          )
          self._test_op(
              mesh,
              lambda x, y, z: x[z, :, :, y],
              torch.randn(16, 32, 16, 12),
              torch.randint(2, (8, 1)),
              torch.randint(5, (12, 8, 12)),
          )
  
imports: import torch
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorConverter, DTensorTestBase, with_comms
label_desc: Use image node_id 18 for calling the DistTensorOpsTest obj's underlying member method code with example usage: obj.test_index() without return types