code: def train_step(state, data):
      (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
      ) = state
      x, y = data
      (loss, non_trainable_variables), grads = grad_fn(
          trainable_variables, non_trainable_variables, x, y
      )
      (
          trainable_variables,
          optimizer_variables,
      ) = optimizer.stateless_apply(
          optimizer_variables, grads, trainable_variables
      )
      # Return updated state
      return loss, (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
      )
  
imports: import os
  import jax
  import numpy
  from keras import Model
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras import ops
  from keras import optimizers
label_desc: Use image node_id 4 for calling a global function with example usage: train_step(state, data) and returns: loss,  trainable_variables,  non_trainable_variables,  optimizer_variables