docstr: Tests that get_arg_info_from_tensor_fqn works as expected for invalid tensor_fqn
  inputs. The string outputs still work but the output module is expected to be None.
docstr_tok: ["Tests","that","get_arg_info_from_tensor_fqn","works","as","expected","for","invalid","tensor_fqn","inputs",".","The","string","outputs","still","work","but","the","output","module","is","expected","to","be","None","."]
code: def test_get_arg_info_from_tensor_fqn_fail(self):
      """
      Tests that get_arg_info_from_tensor_fqn works as expected for invalid tensor_fqn
      inputs. The string outputs still work but the output module is expected to be None.
      """
      for model_class in model_list:
          model = model_class()
          tensor_fqn = "foo.bar.baz"
          arg_info = get_arg_info_from_tensor_fqn(model, tensor_fqn)
          self.assertEqual(arg_info["module"], None)
          self.assertEqual(arg_info["module_fqn"], "foo.bar")
          self.assertEqual(arg_info["tensor_name"], "baz")
          self.assertEqual(arg_info["tensor_fqn"], "foo.bar.baz")
  
file_path: pytorch/test/ao/sparsity/test_sparsity_utils.py
imports: import logging
  import torch
  from torch.ao.pruning.sparsifier.utils import fqn_to_module, get_arg_info_from_tensor_fqn, module_to_fqn
  from torch.testing._internal.common_quantization import ConvBnReLUModel, ConvModel, FunctionalLinear, LinearAddModel, ManualEmbeddingBagLinear, SingleLayerLinearModel, TwoLayerLinearModel
  from torch.testing._internal.common_utils import TestCase
label_desc: Use image node_id 8 for calling the TestSparsityUtilFunctions obj's underlying member method code with example usage: obj.test_get_arg_info_from_tensor_fqn_fail() without return types