code: def test_distribute_tensor_errors(self):
      device_mesh = DeviceMesh(
          self.device_type, torch.arange(self.world_size).reshape(2, 2)
      )
      tensor_shape = [3 * self.world_size, 3 * self.world_size]
      tensor_to_distribute = torch.randn(*tensor_shape)
  
      with self.assertRaisesRegex(
          ValueError, "must have the same length"
      ):
          shard_spec = [Shard(0)]
          distribute_tensor(
              tensor_to_distribute, device_mesh, shard_spec
          )
  
      with self.assertRaisesRegex(
          RuntimeError, "distribute leaf tensor"
      ):
          shard_spec = [Shard(0)]
          global_tensor = torch.randn(*tensor_shape, requires_grad=True)
          global_tensor_to_distribute = global_tensor + 2
          distribute_tensor(
              global_tensor_to_distribute, device_mesh, shard_spec
          )
  
      spec = [Shard(0), Shard(1)]
      dtensor = distribute_tensor(
          tensor_to_distribute, device_mesh, spec
      )
  
      with self.assertRaisesRegex(
          ValueError, "to a different device mesh"
      ):
          new_mesh = DeviceMesh(
              self.device_type, torch.arange(self.world_size)
          )
          distribute_tensor(dtensor, new_mesh, [Shard(0)])
  
      with self.assertRaisesRegex(
          ValueError, "to a different placements"
      ):
          new_spec = [Shard(0), Replicate()]
          distribute_tensor(dtensor, device_mesh, new_spec)
  
file_path: pytorch/test/distributed/_tensor/test_api.py
imports: import torch
  import torch.nn
  from torch.distributed._tensor import DeviceMesh, distribute_module, distribute_tensor, DTensor, Replicate, Shard
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
label_desc: Use image node_id 3 for calling the DTensorAPITest obj's underlying member method code with example usage: obj.test_distribute_tensor_errors() without return types