code: def tfvariable_sequential_updates(_):
      x = variables.Variable(1.0, name="x")
      y = variables.Variable(1.0, name="y")
      updates = control_flow_ops.no_op()
      for _ in range(3):
          with ops.control_dependencies([updates]):
              x_val = x.read_value() + y
              updates = x.assign_sub(0.1 * x_val)
  
      array_ops.identity(updates, name="result")
  
imports: import argparse
  import os
  import sys
  from absl import app
  import six
  from six.moves import range
  from tensorflow.core.protobuf import saver_pb2
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.ops import variables
  from tensorflow.python.training import saver
label_desc: Use image node_id 14 for calling a global function with example usage: tfvariable_sequential_updates(_) without return types