code: def train_step(
      state: nnx.TrainState[MLP],
      inputs: jax.Array,
      labels: jax.Array,
  ):
      def loss_fn(params: nnx.State):
          logits, _ = state.apply(params)(inputs)
          loss = jnp.mean(
              optax.softmax_cross_entropy_with_integer_labels(
                  logits, labels
              )
          )
          return loss
  
      grad_fn = jax.value_and_grad(loss_fn)
      loss, grads = grad_fn(state.params)
      state = state.apply_gradients(grads=grads)
  
      return state, loss
  
imports: import typing
  from functools import partial
  import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  import optax
  from datasets import load_dataset
  from flax.experimental import nnx
label_desc: Use image node_id 8 for calling a global function with example usage: train_step(state, inputs, labels) and returns: state,  loss, loss