code: def train_step(self, data):
      y = data["label"]
  
      with tf.GradientTape() as tape:
          y_pred = self(data, training=True)
          loss = self.compiled_loss(
              y, y_pred, regularization_losses=self.losses
          )
  
      trainable_vars = self.trainable_variables
      gradients = tape.gradient(loss, trainable_vars)
      self.optimizer.apply_gradients(zip(gradients, trainable_vars))
      self.compiled_metrics.update_state(y, y_pred)
      return {m.name: m.result() for m in self.metrics}
  
imports: import math
  import os
  import shutil
  import tempfile
  import pytest
  from unittest import TestCase
  import tensorflow
  import numpy
  from bigdl.orca.data.pandas import read_csv
  from bigdl.orca.data import SparkXShards, XShards
  from bigdl.orca.data.tf.data import Dataset
  from bigdl.orca.learn.tf2 import Estimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca import OrcaContext
label_desc: Use image node_id 3 for calling the SimpleModel obj's underlying member method code with example usage: obj.train_step(data) and returns: unknown