code: def test_dynaimc_conditionals_auto_wrapped():
      if torch_version() < (1, 8, 0):
          pytest.skip("requires torch version >= 1.8.0")
      from fairscale.experimental.nn.auto_shard import shard_model
  
      features = 10
  
      model = BranchedNetwork(features)
      sharded_model = shard_model(model, 3)
      assert len(sharded_model) == 3
  
      input_ = torch.randn(3, features)
      model_output = model(input_)
      sharded_model_output = input_
      for shard in sharded_model:
          sharded_model_output = shard(sharded_model_output)
      assert torch.allclose(model_output, sharded_model_output)
  
imports: import math
  import sys
  import pytest
  import torch
  import torch.nn
  import torch.nn
  from fairscale.internal import torch_version
label_desc: Use image node_id 2 for calling a global function with example usage: test_dynaimc_conditionals_auto_wrapped() without return types