code: def main():
      # Training settings
      parser = argparse.ArgumentParser(
          description="PyTorch MNIST Example"
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=1000,
          metavar="N",
          help="input batch size for testing (default: 1000)",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=1,
          metavar="N",
          help="number of epochs to train (default: 14)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1.0,
          metavar="LR",
          help="learning rate (default: 1.0)",
      )
      parser.add_argument(
          "--gamma",
          type=float,
          default=0.7,
          metavar="M",
          help="Learning rate step gamma (default: 0.7)",
      )
      parser.add_argument(
          "--no-cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--dry-run",
          action="store_true",
          default=False,
          help="quickly check a single pass",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=10,
          metavar="N",
          help="how many batches to wait before logging training status",
      )
      args, _ = parser.parse_known_args()
  
      use_cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = {"batch_size": args.batch_size}
      if use_cuda:
          kwargs.update(
              {"num_workers": 1, "pin_memory": True, "shuffle": True},
          )
  
      transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,)),
          ]
      )
      dataset1 = datasets.MNIST(
          "/tmp/torch_mnist_data",
          train=True,
          download=True,
          transform=transform,
      )
      dataset2 = datasets.MNIST(
          "/tmp/torch_mnist_data", train=False, transform=transform
      )
      train_loader = torch.utils.data.DataLoader(dataset1, **kwargs)
      test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
  
      model = Net().to(device)
      model.load_state_dict(
          torch.load(
              "../mnist_pytorch/mnist_pretrained.pth",
              map_location=lambda storage, location: storage,
          )
      )
      optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
  
      os.makedirs(OUT_DIR, exist_ok=True)
  
      # Example of Sensitivity Analysis usage
      s_analyzer = (
          aup.compression.sensitivity_analysis.SensitivityAnalysis(
              model=model,
              val_func=lambda model: test(model, device, test_loader),
          )
      )
      sensitivity = s_analyzer.analysis(val_args=[model])
      s_analyzer.export(
          os.path.join(OUT_DIR, "sensitivity_analysis.log")
      )
  
      # ChannelDependency
      data = torch.ones(1, 1, 28, 28).to(device)
      channel_depen = (
          aup.compression.shape_dependency.ChannelDependency(
              model, data
          )
      )
      channel_depen.export(
          os.path.join(OUT_DIR, "channel_dependency.csv")
      )
  
file_path: auptimizer/Examples/compression/utility_functions/mnist.py
imports: from __future__ import print_function
  import argparse
  import os
  import sys
  import time
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.optim.lr_scheduler import StepLR
  import aup
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types