class_docstr: Prefer single-proc test runner for basic tests as it is easier to work with.
code: def test_dynamo_trace_allgather_coalesced(self):
      def func(inp, *, tag, ranks, group_size):
          ar = torch.ops.c10d_functional.all_gather_into_tensor_coalesced(
              inp, tag, ranks, group_size
          )
          return ar
  
      inputs = [
          torch.ones(4, 4, device="cuda"),
          torch.ones(6, 6, device="cuda"),
      ]
      counter = CompileCounter()
      compiled = torch.compile(func, backend=counter)
      out = compiled(inputs, **self.get_world_trs())
      correct = func(inputs, **self.get_world_trs())
      assert counter.frame_count == 1
      assert (
          counter.op_count == 3
      )  # It generates 2 getattr to unpack the array
      assert same(out, correct)
  
imports: import functools
  import unittest
  from unittest.mock import patch
  import torch
  from torch._C import FileCheck
  import torch.distributed._functional_collectives
  import torch._dynamo
  import torch._dynamo.test_case
  from torch._dynamo.utils import same
  from torch._dynamo.testing import CompileCounter
  from torch.distributed.distributed_c10d import GroupMember
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing._internal.common_distributed import DynamoDistributedSingleProcTestCase, DynamoDistributedMultiProcTestCase, _dynamo_dist_per_rank_init, requires_nccl, skip_if_lt_x_gpu
  from torch._inductor.compile_fx import compile_fx
  from torch.utils._triton import has_triton
  from torch._inductor.utils import run_and_get_triton_code
  import torch._dynamo.logging
label_desc: Use image node_id 16 for calling the TestCollectivesInductor obj's underlying member method code with example usage: obj.test_dynamo_trace_allgather_coalesced() and returns: ar