code: def test_train_step(self):
      # Get the default configuration.
      config = default.get_config()
  
      # Initialize the network with a dummy graph.
      rng, init_rng = jax.random.split(self.rng)
      init_graphs = next(self.datasets["train"].as_numpy_iterator())
      init_graphs_preprocessed = train.replace_globals(init_graphs)
      init_net = train.create_model(config, deterministic=True)
      params = jax.jit(init_net.init)(
          init_rng, init_graphs_preprocessed
      )
  
      # Create the optimizer.
      optimizer = train.create_optimizer(config)
  
      # Create the training state.
      net = train.create_model(config, deterministic=False)
      state = train_state.TrainState.create(
          apply_fn=net.apply, params=params, tx=optimizer
      )
  
      # Perform one step of updates.
      # We use the same batch of graphs that we used for initialization.
      state, train_metrics = train.train_step(
          state, init_graphs, rngs={"dropout": rng}
      )
  
      # Check that none of the parameters are NaNs!
      params = flax.core.unfreeze(state.params)
      flat_params = {
          "/".join(k): v
          for k, v in flax.traverse_util.flatten_dict(params).items()
      }
      for array in flat_params.values():
          self.assertTrue(jnp.all(~jnp.isnan(array)))
  
      # Check that the metrics are well defined.
      train_metrics_vals = train_metrics.compute()
      self.assertGreaterEqual(train_metrics_vals["loss"], 0)
      self.assertGreaterEqual(train_metrics_vals["accuracy"], 0)
      self.assertLessEqual(train_metrics_vals["accuracy"], 1)
  
imports: import functools
  import pathlib
  import tempfile
  from typing import Dict, Optional
  import warnings
  from absl.testing import absltest
  from absl.testing import parameterized
  import flax
  from flax.training import train_state
  import jax
  from jax import numpy
  import jraph
  import tensorflow
  import tensorflow_datasets
  import numpy
  from configs import default
  from configs import test
  import input_pipeline
  import train
label_desc: Use image node_id 6 for calling the OgbgMolpcbaTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types