code: def train_step(state: nnx.TrainState[MLP], batch):
      x, y = batch
  
      def loss_fn(params):
          y_pred, (updates, _) = state.apply(params, "counts")(x)
          counts = updates.extract(Count)
          loss = jnp.mean((y - y_pred) ** 2)
          return loss, counts
  
      grads, counts = jax.grad(loss_fn, has_aux=True)(state.params)
      # sdg update
      state = state.apply_gradients(grads=grads, counts=counts)
  
      return state
  
file_path: flax/flax/experimental/nnx/examples/toy_examples/03_train_state.py
imports: import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  import optax
  from flax.experimental import nnx
label_desc: Use image node_id 2 for calling a global function with example usage: train_step(state, batch) and returns: state, loss,  counts