code: def test_alexnet_with_input_tensor(self) -> None:
      pretrained_model = models.alexnet(
          weights=AlexNet_Weights.IMAGENET1K_V1
      )
      inp = torch.randn(1, 3, 224, 224)
      ms1 = get_summary_and_prune(
          pretrained_model, max_depth=1, module_args=(inp,)
      )
      ms2 = get_summary_and_prune(
          pretrained_model, max_depth=2, module_args=(inp,)
      )
  
      self.assertEqual(ms1.module_type, "AlexNet")
      self.assertEqual(ms1.num_parameters, 61100840)
      self.assertFalse(ms1.has_uninitialized_param)
      self.assertEqual(ms1.flops_forward, 714188480)
      self.assertEqual(ms1.flops_backward, 1358100160)
      self.assertEqual(ms1.in_size, [1, 3, 224, 224])
      self.assertEqual(ms1.out_size, [1, 1000])
  
      ms_features = ms2.submodule_summaries["features"]
      self.assertEqual(ms_features.module_type, "Sequential")
      self.assertFalse(ms_features.has_uninitialized_param)
      self.assertEqual(ms_features.flops_forward, 655566528)
      self.assertEqual(ms_features.flops_backward, 1240856256)
      self.assertEqual(ms_features.in_size, [1, 3, 224, 224])
      self.assertEqual(ms_features.out_size, [1, 256, 6, 6])
  
      ms_avgpool = ms2.submodule_summaries["avgpool"]
      self.assertEqual(ms_avgpool.module_type, "AdaptiveAvgPool2d")
      self.assertFalse(ms_avgpool.has_uninitialized_param)
      self.assertEqual(ms_avgpool.flops_forward, 0)
      self.assertEqual(ms_avgpool.flops_backward, 0)
      self.assertEqual(ms_avgpool.in_size, [1, 256, 6, 6])
      self.assertEqual(ms_avgpool.out_size, [1, 256, 6, 6])
  
      ms_classifier = ms2.submodule_summaries["classifier"]
      self.assertEqual(ms_classifier.module_type, "Sequential")
      self.assertFalse(ms_classifier.has_uninitialized_param)
      self.assertEqual(ms_classifier.flops_forward, 58621952)
      self.assertEqual(ms_classifier.flops_backward, 117243904)
      self.assertEqual(ms_classifier.in_size, [1, 9216])
      self.assertEqual(ms_classifier.out_size, [1, 1000])
  
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 10 for calling the ModuleSummaryTest obj's underlying member method code with example usage: obj.test_alexnet_with_input_tensor() without return types