code: def train_step(
      state: train_state.TrainState,
      graphs: jraph.GraphsTuple,
      rngs: Dict[str, jnp.ndarray],
  ) -> Tuple[train_state.TrainState, metrics.Collection]:
      """Performs one update step over the current batch of graphs."""
  
      def loss_fn(params, graphs):
          curr_state = state.replace(params=params)
  
          # Extract labels.
          labels = graphs.globals
  
          # Replace the global feature for graph classification.
          graphs = replace_globals(graphs)
  
          # Compute logits and resulting loss.
          logits = get_predicted_logits(curr_state, graphs, rngs)
          mask = get_valid_mask(labels, graphs)
          loss = binary_cross_entropy_with_mask(
              logits=logits, labels=labels, mask=mask
          )
          mean_loss = jnp.sum(jnp.where(mask, loss, 0)) / jnp.sum(mask)
  
          return mean_loss, (loss, logits, labels, mask)
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (_, (loss, logits, labels, mask)), grads = grad_fn(
          state.params, graphs
      )
      state = state.apply_gradients(grads=grads)
  
      metrics_update = TrainMetrics.single_from_model_output(
          loss=loss, logits=logits, labels=labels, mask=mask
      )
      return state, metrics_update
  
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 9 for calling a global function with example usage: train_step(state, graphs, rngs) and returns: state,  metrics_update, mean_loss,  loss,  logits,  labels,  mask