code: def _process_variables(graph, variables, updates):
      import tensorflow as tf
  
      all_trainable_variables = variables
  
      name2idx = dict(
          [
              (v.name, idx)
              for idx, v in enumerate(all_trainable_variables)
          ]
      )
  
      all_variables = graph.get_collection(
          tf.GraphKeys.GLOBAL_VARIABLES
      )
  
      update_ops = graph.get_collection(tf.GraphKeys.UPDATE_OPS)
  
      if updates is not None:
          update_ops += updates
  
      trainable_variables = [0] * len(all_trainable_variables)
      trainable_assigns = [0] * len(all_trainable_variables)
      trainable_variable_placeholders = [0] * len(
          all_trainable_variables
      )
      extra_variables = []
      extra_variable_assigns = []
      extra_variable_assign_placeholders = []
      for v in all_variables:
          p = tf.placeholder(dtype=v.dtype, shape=v.shape)
          a = tf.assign(v, p)
  
          # special treatment for ResourceVariable
          if v.op.type == "VarHandleOp":
              v_float_value = tf.to_float(v.read_value())
          else:
              v_float_value = tf.to_float(v)
  
          if v.name in name2idx:
              trainable_variables[name2idx[v.name]] = v_float_value
              trainable_assigns[name2idx[v.name]] = a
              trainable_variable_placeholders[name2idx[v.name]] = p
          else:
              extra_variables.append(v_float_value)
              extra_variable_assigns.append(a)
              extra_variable_assign_placeholders.append(p)
  
      extra_variable_assign = tf.group(*extra_variable_assigns)
      trainable_assign = tf.group(*trainable_assigns)
      update_op = tf.group(update_ops)
  
      return (
          trainable_variables,
          trainable_variable_placeholders,
          trainable_assign,
          extra_variables,
          extra_variable_assign_placeholders,
          extra_variable_assign,
          update_op,
      )
  
imports: import json
  import logging
  import os
  import sys
  import tempfile
  from bigdl.dllib.nn.criterion import Criterion
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.dllib.utils.common import to_list, JavaValue
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.feature.common import FeatureSet
  from bigdl.dllib.keras.engine.topology import to_bigdl_metric, Loss, OptimMethod
  from bigdl.dllib.net.utils import find_placeholders, to_bigdl_optim_method, find_tensors
  from bigdl.dllib.estimator.estimator import Estimator
  from bigdl.dllib.utils import nest
  from bigdl.dllib.utils.triggers import EveryEpoch
  from bigdl.dllib.utils.triggers import ZooTrigger
  from bigdl.orca.tfpark.tf_dataset import TFNdarrayDataset, check_data_compatible
  from bigdl.orca.tfpark.tf_dataset import _standarize_feature_label_dataset
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 6 for calling the TFModel obj's underlying member method code with example usage: obj._process_variables(graph, variables, updates) and returns: trainable_variables,  trainable_variable_placeholders,  trainable_assign,  extra_variables,  extra_variable_assign_placeholders,  extra_variable_assign,  update_op