docstr: Test that we can record complex example inputs such as lists and dicts
          
docstr_tok: ["Test","that","we","can","record","complex","example","inputs","such","as","lists","and","dicts"]
code: def test_get_fqn_to_example_inputs_complex_args(self):
      """Test that we can record complex example inputs such as lists and dicts"""
  
      class Sub(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear1 = torch.nn.Linear(5, 5)
              self.linear2 = torch.nn.Linear(5, 5)
  
          def forward(self, x, list_arg, dict_arg):
              x = self.linear1(x)
              x = self.linear2(x)
              return x
  
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear1 = torch.nn.Linear(5, 5)
              self.linear2 = torch.nn.Linear(5, 5)
              self.sub = Sub()
  
          def forward(self, x):
              x = self.linear1(x)
              x = self.linear2(x)
              x = self.sub(x, [x], {"3": x})
              return x
  
      example_inputs = (torch.rand(1, 5),)
      m = M().eval()
      fqn_to_example_inputs = get_fqn_to_example_inputs(
          m, example_inputs
      )
      assert "sub" in fqn_to_example_inputs
      assert isinstance(fqn_to_example_inputs["sub"][1], list)
      assert (
          isinstance(fqn_to_example_inputs["sub"][2], dict)
          and "3" in fqn_to_example_inputs["sub"][2]
      )
  
file_path: pytorch/test/quantization/core/test_utils.py
imports: import torch
  from torch.testing._internal.common_utils import TestCase
  from torch.ao.quantization.utils import get_fqn_to_example_inputs
  from torch.ao.nn.quantized.modules.utils import _quantize_weight
  from torch.ao.quantization import MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver
label_desc: Use image node_id 4 for calling the TestUtils obj's underlying member method code with example usage: obj.test_get_fqn_to_example_inputs_complex_args() and returns: x, x