code: def apply_model(state, inputs, labels):
      """Computes gradients, loss and accuracy for a single batch."""
  
      def loss_fn(params):
          logits = state.apply_fn({"params": params}, inputs)
          one_hot = jax.nn.one_hot(labels, _NUM_CLASSES.value)
          loss = jnp.mean(
              optax.softmax_cross_entropy(logits=logits, labels=one_hot)
          )
          return loss, logits
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (loss, logits), grads = grad_fn(state.params)
      accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)
      return grads, loss, accuracy
  
imports: from absl import app
  from absl import flags
  import os
  import time
  from flax import linen
  from flax.training import train_state
  import jax
  from jax import lax
  from jax import numpy
  import optax
  import numpy
  import tensorflow
  import tensorflowjs
  import input_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: apply_model(state, inputs, labels) and returns: grads,  loss,  accuracy, loss,  logits