code: def main(args):
      if args.seed:
          torch.manual_seed(0)
          random.seed(0)
  
      assert args.norm_type in [None, "layer_norm", "batch_norm"]
  
      print(
          "dataset,num_samples,dimensionality,objective_diff,objective_pytorch,"
          + "objective_sklearn,pytorch_time,sklearn_time,pytorch_l2_val,sklearn_l2_val"
      )
      for dataset in args.datasets:
          dataset_fn = getattr(datasets, dataset)
          data = dataset_fn(return_X_y=True)
  
          (
              train_loader,
              val_loader,
              in_features,
              num_samples,
          ) = sklearn_dataset_to_loaders(
              data,
              batch_size=args.batch_size,
              num_workers=args.workers,
              shuffle=args.shuffle,
              one_hot=args.one_hot,
          )
  
          (
              similarity,
              sklearn_stats,
              pytorch_stats,
          ) = compare_to_sk_learn(
              alpha=args.alpha,
              max_epoch=args.max_epoch,
              train_loader=train_loader,
              val_loader=val_loader,
              train_prop=args.training_prop,
              pytorch_model_type=args.pytorch_model_type,
              sklearn_model_type=args.sklearn_model_type,
              norm_type=args.norm_type,
              init_scheme=args.init_scheme,
              objective=args.objective,
          )
  
          print(
              f"{dataset},{num_samples},{in_features},{similarity['objective_rel_diff']},"
              + f"{similarity['objective_pytorch']},{similarity['objective_sklearn']},"
              + f"{pytorch_stats['train_time']},{sklearn_stats['train_time']},"
              + f"{pytorch_stats['l2']},{sklearn_stats['l2']}"
          )
  
file_path: captum/tests/utils/models/linear_models/_test_linear_classifier.py
imports: import argparse
  import random
  from typing import Optional
  import captum._utils.models.linear_model.model
  import numpy
  import sklearn.datasets
  import torch
  from tests.utils.test_linear_model import _evaluate
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types