code: def BuildSplittableModel():
      """Build a small model that can be run partially in each step."""
      image = array_ops.zeros([2, 6, 6, 3])
  
      kernel1 = variable_scope.get_variable(
          "DW",
          [3, 3, 3, 6],
          dtypes.float32,
          initializer=init_ops.random_normal_initializer(stddev=0.001),
      )
      r1 = nn_ops.conv2d(image, kernel1, [1, 2, 2, 1], padding="SAME")
  
      kernel2 = variable_scope.get_variable(
          "DW2",
          [2, 3, 3, 6],
          dtypes.float32,
          initializer=init_ops.random_normal_initializer(stddev=0.001),
      )
      r2 = nn_ops.conv2d(image, kernel2, [1, 2, 2, 1], padding="SAME")
  
      r3 = r1 + r2
      return r1, r2, r3
  
imports: import contextlib
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_grad
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import rnn
  from tensorflow.python.ops import rnn_cell
  from tensorflow.python.ops import tensor_array_grad
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.profiler import model_analyzer
  from tensorflow.python.training import gradient_descent
  from tensorflow.python.util import _pywrap_tfprof
  from tensorflow.python.util import compat
label_desc: Use image node_id 3 for calling a global function with example usage: BuildSplittableModel() and returns: r1,  r2,  r3