code: def train_step(self, state, data):
      (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
          metrics_variables,
      ) = state
      x, y = data
  
      # Get the gradient function.
      grad_fn = jax.value_and_grad(
          self.compute_loss_and_updates, has_aux=True
      )
  
      # Compute the gradients.
      (loss, (y_pred, non_trainable_variables)), grads = grad_fn(
          trainable_variables,
          non_trainable_variables,
          x,
          y,
          training=True,
      )
  
      # Update trainable variables and optimizer variables.
      (
          trainable_variables,
          optimizer_variables,
      ) = self.optimizer.stateless_apply(
          optimizer_variables, grads, trainable_variables
      )
  
      # Update metrics.
      new_metrics_vars = []
      for metric in self.metrics:
          this_metric_vars = metrics_variables[
              len(new_metrics_vars) : len(new_metrics_vars)
              + len(metric.variables)
          ]
          if metric.name == "loss":
              this_metric_vars = metric.stateless_update_state(
                  this_metric_vars, loss
              )
          else:
              this_metric_vars = metric.stateless_update_state(
                  this_metric_vars, y, y_pred
              )
          logs = metric.stateless_result(this_metric_vars)
          new_metrics_vars += this_metric_vars
  
      # Return metric logs and updated state variables.
      state = (
          trainable_variables,
          non_trainable_variables,
          optimizer_variables,
          new_metrics_vars,
      )
      return logs, state
  
file_path: keras/guides/custom_train_step_in_jax.py
imports: import os
  import jax
  import keras
  import numpy
label_desc: Use image node_id 2 for calling the CustomModel obj's underlying member method code with example usage: obj.train_step(state, data) and returns: logs,  state