code: def _test_ddp_gather_uneven_tensors_multidim(
      rank: int, worldsize: int = NUM_PROCESSES
  ) -> None:
      tensor = torch.ones(rank + 1, 2 - rank)
      result = gather_all_tensors(tensor)
      assert len(result) == worldsize
      for idx in range(worldsize):
          val = result[idx]
          assert (val == torch.ones_like(val)).all()
  
imports: import os
  import sys
  from copy import deepcopy
  from functools import partial
  import pytest
  import torch
  from torch import tensor
  from torchmetrics import Metric
  from torchmetrics.utilities.distributed import gather_all_tensors
  from torchmetrics.utilities.exceptions import TorchMetricsUserError
  from unittests import NUM_PROCESSES
  from unittests.helpers import seed_all
  from unittests.helpers.testers import DummyListMetric, DummyMetric, DummyMetricSum
label_desc: Use image node_id 5 for calling a global function with example usage: _test_ddp_gather_uneven_tensors_multidim(rank, worldsize) without return types