code: def main(argv):
      if len(argv) > 1:
          raise app.UsageError("Too many command-line arguments.")
  
      base_model_path = "/tmp/jax2tf/tf_js_quickdraw"
      dataset_path = os.path.join(base_model_path, "data")
      classes = input_pipeline.download_dataset(
          dataset_path, _NUM_CLASSES.value
      )
      assert (
          len(classes) == _NUM_CLASSES.value
      ), "Incorrect number of classes"
      print(f"Classes are: {classes}")
      print("Loading dataset into memory...")
      train_ds, test_ds = input_pipeline.get_datasets(
          dataset_path, classes
      )
      print(f"Starting training for {_NUM_EPOCHS.value} epochs...")
  
      state = create_train_state(jax.random.PRNGKey(0))
      state = train(state, train_ds, test_ds)
  
      tfjs.converters.convert_jax(
          apply_fn=state.apply_fn,
          params={"params": state.params},
          input_signatures=[tf.TensorSpec([1, 28, 28, 1])],
          model_dir=os.path.join(base_model_path, "tfjs_models"),
      )
  
imports: from absl import app
  from absl import flags
  import os
  import time
  from flax import linen
  from flax.training import train_state
  import jax
  from jax import lax
  from jax import numpy
  import optax
  import numpy
  import tensorflow
  import tensorflowjs
  import input_pipeline
label_desc: Use image node_id 6 for calling a global function with example usage: main(argv) without return types