code: def main(save_path: Optional[Path], error_path: Optional[Path]):
      table = PrettyTable()
      entries = defaultdict(list)
  
      print("CUDA device: ", torch.cuda.get_device_name(0))
      iters = 100
      header = None
      batch_sizes = [16, 32, 64, 128, 256]
      sequence_lengths = [64, 128, 256, 512]
      embed_dims = [512, 1024]
      num_heads_list = [8, 16]
      betas = range(1, 64, 4)
  
      for (
          batch_size,
          sequence_length,
          embed_dim,
          num_heads,
          block_size,
          b,
      ) in tqdm(
          list(
              itertools.product(
                  batch_sizes,
                  sequence_lengths,
                  embed_dims,
                  num_heads_list,
                  [2],
                  betas,
              )
          )
      ):
          seed = (
              26214  # Magic number that works well for higher b values
          )
          entry = run(
              1,
              b * 0.05,
              iters,
              batch_size,
              sequence_length,
              embed_dim,
              num_heads,
              "cuda",
              torch.float16,
              block_size,
              seed,
          )
          if entry is None:
              continue
          if header is None:
              table.field_names = list(entry.keys())
              header = list(entry.keys())
          row = []
          for k, v in entry.items():
              row.append(v)
              entries[k].append(v)
          table.add_row(row)
  
      # Print the full table to console
      print(table)
      pprint(error_dict)
  
      csv_string = table.get_csv_string()
      if save_path is not None:
          with open(save_path, "w") as csvfile:
              csvfile.write(csv_string)
  
      print(f"Total errors: {sum(error_dict.values())}")
      if error_path is not None:
          with open(error_path, "w") as file:
              file.write(json.dumps(error_dict))
  
file_path: pytorch/benchmarks/transformer/better_transformer_vs_mha_functional.py
imports: import argparse
  import itertools
  import json
  import random
  import warnings
  from collections import defaultdict, OrderedDict
  from pathlib import Path
  from pprint import pprint
  from typing import Optional
  import numpy
  import torch
  from prettytable import PrettyTable
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: main(save_path, error_path) without return types