code: def apply_model(
      state: train_state.TrainState,
      image: jnp.DeviceArray,
      labels: jnp.DeviceArray,
  ) -> tuple[
      t.Callable[..., core.FrozenDict[str, t.Any]],
      jnp.ndarray,
      jnp.ndarray,
  ]:
      """Compute gradients, loss, and accuracy for a single batch."""
  
      def loss_fn(
          params: core.FrozenDict[str, t.Any]
      ) -> tuple[jnp.ndarray, jnp.ndarray]:
          logits: jnp.ndarray = state.apply_fn(
              {"params": params}, image
          )
          one_hot = jax.nn.one_hot(labels, 10)  # 0 to 9
          loss: jnp.ndarray = jnp.mean(
              optax.softmax_cross_entropy(logits=logits, labels=one_hot)
          )
          return loss, logits
  
      grad = jax.value_and_grad(loss_fn, has_aux=True)
      (loss, logits), grad = grad(state.params)
      accuracy: jnp.ndarray = jnp.mean(jnp.argmax(logits, -1) == labels)
      return grad, loss, accuracy
  
imports: from __future__ import annotations
  import argparse
  import os
  import typing
  from typing import TYPE_CHECKING
  import attrs
  import cattrs
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow_datasets
  from flax import linen
  from flax import serialization
  from flax.metrics import tensorboard
  from flax.training import train_state
  import bentoml
  from bentoml._internal.utils.pkg import pkg_version_info
label_desc: Use image node_id 1 for calling a global function with example usage: apply_model(state, image, labels) and returns: grad,  loss,  accuracy, loss,  logits