code: def evaluate(data_path, master_csv_path, split_path, save_dir):
      """Evaluation Script."""
      logging.info("Evaluating OGB molviv")
      logging.info("Dataset split: %s", split_path)
      # Initialize the dataset reader.
      reader = data_utils.DataReader(
          data_path=data_path,
          master_csv_path=master_csv_path,
          split_path=split_path,
          batch_size=1,
          dynamically_batch=True,
      )
      # Transform impure `net_fn` to pure functions with hk.transform.
      net = hk.without_apply_rng(hk.transform(net_fn))
      with pathlib.Path(save_dir, "molhiv.pkl").open("rb") as fp:
          params = pickle.load(fp)
      accumulated_loss = 0
      accumulated_accuracy = 0
      idx = 0
  
      # We pmap the computation of our loss, since this is the main computation.
      compute_loss_fn = jax.pmap(
          functools.partial(compute_loss, net=net)
      )
      for graph_batch in device_batch(reader):
          label = graph_batch.globals["label"]
          loss, acc = compute_loss_fn(params, graph_batch, label)
          accumulated_accuracy += jnp.sum(acc)
          accumulated_loss += jnp.sum(loss)
          total_num_padding_graphs = jnp.sum(
              jax.vmap(jraph.get_number_of_padding_with_graphs_graphs)(
                  graph_batch
              )
          )
          idx += graph_batch.n_node.size - total_num_padding_graphs
          if idx % 100 == 0:
              logging.info("Evaluated %s graphs", idx)
          logging.info("Completed evaluation.")
      loss = accumulated_loss / idx
      accuracy = accumulated_accuracy / idx
      logging.info("Eval loss: %s, accuracy %s", loss, accuracy)
      return loss, accuracy
  
imports: import functools
  import logging
  import pathlib
  import pickle
  from typing import Iterator
  from absl import app
  from absl import flags
  import haiku
  import jax
  import jax.numpy
  import jraph
  from jraph.ogb_examples import data_utils
  import optax
label_desc: Use image node_id 8 for calling a global function with example usage: evaluate(data_path, master_csv_path, split_path, save_dir) and returns: loss,  accuracy