code: def evaluate_model(
      state: train_state.TrainState,
      datasets: Dict[str, tf.data.Dataset],
      splits: Iterable[str],
  ) -> Dict[str, metrics.Collection]:
      """Evaluates the model on metrics over the specified splits."""
  
      # Loop over each split independently.
      eval_metrics = {}
      for split in splits:
          split_metrics = None
  
          # Loop over graphs.
          for graphs in datasets[split].as_numpy_iterator():
              split_metrics_update = evaluate_step(state, graphs)
  
              # Update metrics.
              if split_metrics is None:
                  split_metrics = split_metrics_update
              else:
                  split_metrics = split_metrics.merge(
                      split_metrics_update
                  )
          eval_metrics[split] = split_metrics
  
      return eval_metrics
  
imports: import os
  from typing import Any, Dict, Iterable, Tuple, Optional
  from absl import logging
  from clu import checkpoint
  from clu import metric_writers
  from clu import metrics
  from clu import parameter_overview
  from clu import periodic_actions
  import flax
  import flax.core
  import flax.linen
  from flax.training import train_state
  import jax
  import jax.numpy
  import jraph
  import ml_collections
  import numpy
  import optax
  import sklearn.metrics
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 11 for calling a global function with example usage: evaluate_model(state, datasets, splits) and returns: eval_metrics