code: def run():
      torch.backends.cudnn.deterministic = True
      random.seed(2022)
      np.random.seed(2022)
      torch.manual_seed(2022)
      torch.cuda.manual_seed_all(2022)
      wandb.login()
  
      wandb_init = {"project": "raytune-colab"}
  
      analysis = tune.run(
          train_mnist,
          loggers=[WandbLogger],
          resources_per_trial={"gpu": 0},
          time_budget_s=5 * 60,
          max_concurrent_trials=1,
          config={
              # wandb dict accepts all arguments that can be passed in wandb.init()
              "wandb": wandb_init,
              # hyperparameters are set by keyword arguments
              "lr": tune.grid_search([0.0001, 0.001, 0.1]),
              "momentum": tune.grid_search([0.9, 0.99]),
          },
      )
  
      print(
          "Best config: ",
          analysis.get_best_config(metric="mean_accuracy", mode="max"),
      )
  
file_path: wandb/tests/functional_tests/t0_main/raytune/t5_colab.py
imports: import random
  import numpy
  import torch
  import torch.optim
  import wandb
  from ray import tune
  from ray.tune.examples.mnist_pytorch import ConvNet, get_data_loaders, test, train
  from ray.tune.integration.wandb import WandbLogger, wandb_mixin
label_desc: Use image node_id 2 for calling a global function with example usage: run() without return types