code: def main():
      # Create experiment
      experiment.create(name="evidence_mnist")
      # Create configurations
      conf = Configs()
      # Load configurations
      experiment.configs(
          conf,
          {
              "optimizer.optimizer": "Adam",
              "optimizer.learning_rate": 0.001,
              "optimizer.weight_decay": 0.005,
              # 'loss_func': 'max_likelihood_loss',
              # 'loss_func': 'cross_entropy_bayes_risk',
              "loss_func": "squared_error_bayes_risk",
              "outputs_to_evidence": "softplus",
              "dropout": 0.5,
          },
      )
      # Start the experiment and run the training loop
      with experiment.start():
          conf.run()
  
imports: from typing import Any
  import torch.nn
  import torch.utils.data
  from labml import tracker, experiment
  from labml.configs import option, calculate
  from labml_helpers.module import Module
  from labml_helpers.schedule import Schedule, RelativePiecewise
  from labml_helpers.train_valid import BatchIndex
  from labml_nn.experiments.mnist import MNISTConfigs
  from labml_nn.uncertainty.evidence import KLDivergenceLoss, TrackStatistics, MaximumLikelihoodLoss, CrossEntropyBayesRisk, SquaredErrorBayesRisk
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types