code: def _get_vars_grads_from_train_op(train_op):
      def predicate(t):
          return t.name.split("/")[-1].startswith(
              "zoo_identity_op_for_grad"
          )
  
      grads = find_tensors([train_op], predicate)
      grad_ops = [grad.op for grad in grads]
      variables = []
      for grad in grad_ops:
          var = list(grad.control_inputs)[0]
          if var.name == "VarHandleOp":
              variables.append(var)
          else:
              variables.append(list(var.outputs)[0])
      # variables = [grad.op.control_inputs[0].outputs[0] for grad in grads]
      return grads, variables
  
imports: import json
  import logging
  import os
  import sys
  import tempfile
  from bigdl.nn.criterion import Criterion
  from bigdl.nn.layer import Layer
  from bigdl.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.util.common import to_list, JavaValue
  from zoo.common.utils import callZooFunc
  from zoo.feature.common import FeatureSet
  from zoo.pipeline.api.keras.engine.topology import to_bigdl_metric, Loss, OptimMethod
  from zoo.pipeline.api.net.utils import find_placeholders, to_bigdl_optim_method, find_tensors
  from zoo.pipeline.estimator import Estimator
  from zoo.util import nest
  from zoo.util.triggers import EveryEpoch
  from zoo.util.triggers import ZooTrigger
  from zoo.tfpark.tf_dataset import TFNdarrayDataset, check_data_compatible
  from zoo.tfpark.tf_dataset import _standarize_feature_label_dataset
label_desc: Use image node_id 6 for calling the TFOptimizer obj's underlying member method code with example usage: obj._get_vars_grads_from_train_op(train_op) and returns: grads,  variables, t