code: def test_module_summary_multiple_inputs(self) -> None:
      class SimpleConv(torch.nn.Module):
          def __init__(self):
              super(SimpleConv, self).__init__()
              self.features = torch.nn.Sequential(
                  torch.nn.Conv2d(
                      1, 1, kernel_size=3, stride=1, padding=1
                  ),
                  torch.nn.ReLU(inplace=True),
              )
              self.offset = 1
  
          def forward(self, x, y, offset=1):
              self.offset = offset
              x1 = self.features(x)
              x2 = self.features(y)
              x = torch.cat((x1, x2), 1)
              return x
  
      model = SimpleConv()
      x = torch.randn(1, 1, 224, 224)
      y = torch.randn(1, 1, 224, 224)
  
      ms1 = get_summary_and_prune(
          model, max_depth=3, module_args=(x, y)
      )
  
      self.assertEqual(ms1.module_type, "SimpleConv")
      self.assertEqual(ms1.num_parameters, 10)
      self.assertFalse(ms1.has_uninitialized_param)
      self.assertEqual(
          ms1.in_size, [[1, 1, 224, 224], [1, 1, 224, 224]]
      )
      self.assertEqual(ms1.out_size, [1, 2, 224, 224])
  
      ms_features = ms1.submodule_summaries["features"]
      self.assertEqual(ms_features.module_type, "Sequential")
      self.assertFalse(ms_features.has_uninitialized_param)
      self.assertEqual(ms_features.in_size, [1, 1, 224, 224])
      self.assertEqual(ms_features.out_size, [1, 1, 224, 224])
  
      ms_avgpool = ms1.submodule_summaries[
          "features"
      ].submodule_summaries["features.0"]
      self.assertEqual(ms_avgpool.module_type, "Conv2d")
      self.assertFalse(ms_avgpool.has_uninitialized_param)
      self.assertEqual(ms_avgpool.in_size, [1, 1, 224, 224])
      self.assertEqual(ms_avgpool.out_size, [1, 1, 224, 224])
  
      ms_classifier = ms1.submodule_summaries[
          "features"
      ].submodule_summaries["features.1"]
      self.assertEqual(ms_classifier.module_type, "ReLU")
      self.assertFalse(ms_classifier.has_uninitialized_param)
      self.assertEqual(ms_classifier.flops_forward, 0)
      self.assertEqual(ms_classifier.flops_backward, 0)
      self.assertEqual(ms_classifier.in_size, [1, 1, 224, 224])
      self.assertEqual(ms_classifier.out_size, [1, 1, 224, 224])
  
imports: import unittest
  from typing import Any, Dict, Optional, Tuple
  import torch
  from torchtnt.utils.module_summary import _get_human_readable_count, get_module_summary, ModuleSummary, prune_module_summary
  from torchvision import models
  from torchvision.models.alexnet import AlexNet_Weights
  from torchvision.models.resnet import ResNet18_Weights
label_desc: Use image node_id 12 for calling the ModuleSummaryTest obj's underlying member method code with example usage: obj.test_module_summary_multiple_inputs() and returns: x