code: def test_output_feature_utils():
      tensor_dict = {}
      output_feature_utils.set_output_feature_tensor(
          tensor_dict, "feature_1", "1", torch.Tensor([1])
      )
      output_feature_utils.set_output_feature_tensor(
          tensor_dict, "feature_1", "10", torch.Tensor([10])
      )
      output_feature_utils.set_output_feature_tensor(
          tensor_dict, "feature_2", "2", torch.Tensor([2])
      )
      output_feature_utils.set_output_feature_tensor(
          tensor_dict, "feature_2", "20", torch.Tensor([20])
      )
  
      assert list(tensor_dict.keys()) == [
          "feature_1::1",
          "feature_1::10",
          "feature_2::2",
          "feature_2::20",
      ]
      assert output_feature_utils.get_output_feature_tensor(
          tensor_dict, "feature_1", "1"
      ) == torch.Tensor([1])
      assert list(
          output_feature_utils.get_single_output_feature_tensors(
              tensor_dict, "feature_1"
          ).keys()
      ) == ["1", "10"]
      assert (
          list(
              output_feature_utils.get_single_output_feature_tensors(
                  tensor_dict, "feature_3"
              ).keys()
          )
          == []
      )
      with pytest.raises(Exception):
          output_feature_utils.get_output_feature_tensor(
              tensor_dict, "feature_1", "2"
          )
  
imports: import pytest
  import torch
  from ludwig.utils import output_feature_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_output_feature_utils() without return types