def METHOD_NAME(placeholder_resolution, convert_variables_to_constants):
  """Convolutional neural network for test.
  Args:
    placeholder_resolution: Resolution to use for input placeholder. Used for
      height and width dimensions.
    convert_variables_to_constants: Whether to convert variables to constants.
  Returns:
    name_to_node: Dict keyed by node name, each entry containing the node's
      NodeDef.
  """
  g = ops.Graph()
  sess = session.Session(graph=g)
  with g.as_default():
    # An input test image with unknown spatial resolution.
    x = array_ops.placeholder(
        dtypes.float32, (1, placeholder_resolution, placeholder_resolution, 1),
        name='input_image')
    # Left branch before first addition.
    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
    # Right branch before first addition.
    l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]], name='L2_pad')
    l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
    l3 = slim.max_pool2d(l2, [3, 3], stride=2, scope='L3', padding='SAME')
    # First addition.
    l4 = nn.relu(l1 + l3, name='L4_relu')
    # Left branch after first addition.
    l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
    # Right branch after first addition.
    l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
    # Final addition.
    gen_math_ops.add(l5, l6, name='L7_add')
    if convert_variables_to_constants:
      sess.run(variables.global_variables_initializer())
      graph_def = graph_util.convert_variables_to_constants(
          sess, g.as_graph_def(), ['L7_add'])
    else:
      graph_def = g.as_graph_def()
  name_to_node = graph_compute_order.parse_graph_nodes(graph_def)
  return name_to_node