code: def main():
      configs = Configs()
      experiment.create(name="sketch_rnn")
  
      # Pass a dictionary of configurations
      experiment.configs(
          configs,
          {
              "optimizer.optimizer": "Adam",
              # We use a learning rate of `1e-3` because we can see results faster.
              # Paper had suggested `1e-4`.
              "optimizer.learning_rate": 1e-3,
              # Name of the dataset
              "dataset_name": "bicycle",
              # Number of inner iterations within an epoch to switch between training, validation and sampling.
              "inner_iterations": 10,
          },
      )
  
      with experiment.start():
          # Run the experiment
          configs.run()
  
imports: import math
  from typing import Optional, Tuple, Any
  import numpy
  import torch
  import torch.nn
  from matplotlib import pyplot
  from torch import optim
  from torch.utils.data import Dataset, DataLoader
  import einops
  from labml import lab, experiment, tracker, monit
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
  from labml_helpers.optimizer import OptimizerConfigs
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types