code: def load_checkpoint(self, path, version):
      # todo make version optional
      model_path = os.path.join(path, "model.{}".format(version))
      optim_method_path = os.path.join(
          path, "optimMethod-TFParkTraining.{}".format(version)
      )
      self.tf_model.training_helper_layer.load_checkpoint(model_path)
      self.optim_method = OptimMethod.load(optim_method_path)
      self.estimator = Estimator(
          self.tf_model.training_helper_layer,
          self.optim_method,
          self.model_dir,
      )
      if self.clip_norm:
          self.estimator.set_l2_norm_gradient_clipping(self.clip_norm)
      if self.clip_constant:
          min_value, max_value = self.clip_constant
          self.estimator.set_constant_gradient_clipping(
              min_value, max_value
          )
  
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 2 for calling the TFOptimizer obj's underlying member method code with example usage: obj.load_checkpoint(path, version) without return types