code: def main():
      config_file = Path(os.getenv("CONFIG_FILE", "config.json"))
      if config_file.exists():
          with open(config_file) as f:
              config = json.load(f)
      else:
          config = {}
      parser = ArgumentParser()
      parser.add_argument("--batch_size", type=int, default=1)
      parser.add_argument("--max_epochs", type=int, default=1)
      parser.add_argument("--action_memory", type=int, default=1)
      parser.add_argument("--optimizer", type=str, default="adamw")
      parser.add_argument("--weight_decay", type=float, default=1e-5)
      parser.add_argument("--lr", type=float, default=1e-4)
      parser.add_argument("--lr_decay_factor", type=float, default=0.5)
      parser.add_argument("--lr_decay_steps", type=int, default=5000)
      parser.add_argument("--device", type=str, default="cuda")
      # parser.add_argument("--half", action="store_true")
      parser.add_argument("--len_data", type=int, default=100)
      parser.add_argument("--pct_synth", type=float, default=0.5)
      parser.add_argument("--n_synth_data", type=int, default=100)
      parser.add_argument("--limit_rank", type=int, default=15)
      parser.add_argument("--alpha", type=float, default=1.0)
      parser.add_argument("--beta", type=float, default=1.0)
      parser.add_argument("--random_seed", type=int, default=None)
      parser.add_argument("--checkpoint_dir", type=str, default=None)
      parser.add_argument(
          "--checkpoint_data_dir", type=str, default=None
      )
      parser.add_argument("--matrix_size", type=int, default=3)
      parser.add_argument("--embed_dim", type=int, default=1024)
      parser.add_argument("--actions_sampled", type=int, default=10)
      parser.add_argument("--n_actors", type=int, default=1)
      parser.add_argument("--mc_n_sim", type=int, default=100)
      parser.add_argument("--n_cob", type=int, default=100000)
      parser.add_argument(
          "--cob_prob", type=float, default=0.9983
      )  # 1 - 0.0017
      parser.add_argument("--data_augmentation", action="store_true")
      parser.add_argument("--cardinality_vector", type=int, default=5)
      parser.add_argument(
          "--n_bar",
          type=int,
          default=100,
          help="N_bar parameter for policy temperature.",
      )
      parser.add_argument("--save_dir", type=str, default=None)
      parser.add_argument(
          "extra_devices", nargs="*", type=str, default=[]
      )
      parser.set_defaults(**config)
      args = parser.parse_args()
  
      cardinality_vector = args.cardinality_vector
      N_bar = args.n_bar
      input_size = args.matrix_size**2
      n_steps = _compute_largest_divisor(input_size)
      n_actions = cardinality_vector ** (3 * input_size // n_steps)
      loss_params = (args.alpha, args.beta)
  
      train_alpha_tensor(
          tensor_length=args.action_memory + 1,
          input_size=input_size,
          scalars_size=1,
          emb_dim=args.embed_dim,
          n_steps=n_steps,
          n_logits=n_actions,
          n_samples=args.actions_sampled,
          device=args.device,
          len_data=args.len_data,
          n_synth_data=args.n_synth_data,
          pct_synth=args.pct_synth,
          batch_size=args.batch_size,
          epochs=args.max_epochs,
          lr=args.lr,
          lr_decay_factor=args.lr_decay_factor,
          lr_decay_steps=args.lr_decay_steps,
          weight_decay=args.weight_decay,
          optimizer_name=args.optimizer,
          loss_params=loss_params,
          limit_rank=args.limit_rank,
          random_seed=args.random_seed,
          checkpoint_dir=args.checkpoint_dir,
          checkpoint_data_dir=args.checkpoint_data_dir,
          n_actors=args.n_actors,
          mc_n_sim=args.mc_n_sim,
          n_cob=args.n_cob,
          cob_prob=args.cob_prob,
          data_augmentation=args.data_augmentation or False,
          N_bar=N_bar,
          extra_devices=args.extra_devices,
          save_dir=args.save_dir,
      )
  
imports: import json
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from open_alpha_tensor import train_alpha_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types