code: def test_evaluate_step(self):
      # Get the default configuration.
      config = default.get_config()
  
      # Initialize the network with a dummy graph.
      _, init_rng = jax.random.split(self.rng)
      init_graphs = next(self.datasets["train"].as_numpy_iterator())
      init_graphs_preprocessed = init_graphs._replace(
          globals=jnp.zeros([init_graphs.n_node.shape[0], 1])
      )
      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 evaluation state.
      eval_net = train.create_model(config, deterministic=True)
      eval_state = train_state.TrainState.create(
          apply_fn=eval_net.apply, params=params, tx=optimizer
      )
  
      # Perform one step of evaluation.
      # We use the same batch of graphs that we used for initialization.
      evaluate_metrics = train.evaluate_step(eval_state, init_graphs)
  
      # Check that the metrics are well defined.
      evaluate_metrics_vals = evaluate_metrics.compute()
      self.assertGreaterEqual(evaluate_metrics_vals["loss"], 0)
      self.assertGreaterEqual(evaluate_metrics_vals["accuracy"], 0)
      self.assertLessEqual(evaluate_metrics_vals["accuracy"], 1)
      self.assertTrue(
          np.isnan(evaluate_metrics_vals["mean_average_precision"])
      )
  
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 7 for calling the OgbgMolpcbaTrainTest obj's underlying member method code with example usage: obj.test_evaluate_step() without return types