code: def test_bundled_input_with_dynamic_type(self):
      class Model(torch.nn.Module):
          def forward(
              self,
              x: Dict[int, torch.Tensor],
              y: Dict[int, torch.Tensor],
              z: Dict[int, torch.Tensor],
          ):
              return x
  
      model = Model()
      script_module = torch.jit.script(model)
  
      sample_input = {
          script_module.forward: [
              (
                  {0: torch.ones(1)},
                  {1: torch.ones(1)},
                  {2: torch.ones(1)},
              )
          ]
      }
  
      bundled_model = torch.utils.bundled_inputs.bundle_inputs(
          script_module, sample_input
      )
  
      buf = bundled_model._save_to_buffer_for_lite_interpreter()
      mobile_module = _load_for_lite_interpreter(io.BytesIO(buf))
  
      i = mobile_module.run_method("get_all_bundled_inputs")
  
      self.assertEqual(
          i[0],
          (
              {0: torch.ones(1)},
              {1: torch.ones(1)},
              {2: torch.ones(1)},
          ),
      )
  
imports: import torch
  import torch.utils.bundled_inputs
  import io
  from tempfile import TemporaryFileName
  from typing import Dict, List
  import inspect
  from torch.testing import FileCheck
  from torch.jit.mobile import _load_for_lite_interpreter, _export_operator_list
  from torch.testing._internal.common_utils import TestCase, run_tests
  from torch.testing._internal.common_quantization import AnnotatedSingleLayerLinearModel, TwoLayerLinearModel, AnnotatedNestedModel
  from torch.testing._internal.common_quantization import QuantizationLiteTestCase
label_desc: Use image node_id 5 for calling the TestLiteScriptQuantizedModule obj's underlying member method code with example usage: obj.test_bundled_input_with_dynamic_type() and returns: x