code: def evaluate_step(
      state: train_state.TrainState,
      graphs: jraph.GraphsTuple,
  ) -> metrics.Collection:
      """Computes metrics over a set of graphs."""
  
      # The target labels our model has to predict.
      labels = graphs.globals
  
      # Replace the global feature for graph classification.
      graphs = replace_globals(graphs)
  
      # Get predicted logits, and corresponding probabilities.
      logits = get_predicted_logits(state, graphs, rngs=None)
  
      # Get the mask for valid labels and graphs.
      mask = get_valid_mask(labels, graphs)
  
      # Compute the various metrics.
      loss = binary_cross_entropy_with_mask(
          logits=logits, labels=labels, mask=mask
      )
  
      return EvalMetrics.single_from_model_output(
          loss=loss, logits=logits, labels=labels, mask=mask
      )
  
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 10 for calling a global function with example usage: evaluate_step(state, graphs) and returns: EvalMetrics