code: def _stacked_separable_conv(
      net, stride, operation, filter_size, use_bounded_activation
  ):
      """Takes in an operations and parses it to the correct sep operation."""
      num_layers, kernel_size = _operation_to_info(operation)
      activation_fn = (
          tf.nn.relu6 if use_bounded_activation else tf.nn.relu
      )
      for layer_num in range(num_layers - 1):
          net = activation_fn(net)
          net = slim.separable_conv2d(
              net,
              filter_size,
              kernel_size,
              depth_multiplier=1,
              scope="separable_{0}x{0}_{1}".format(
                  kernel_size, layer_num + 1
              ),
              stride=stride,
          )
          net = slim.batch_norm(
              net,
              scope="bn_sep_{0}x{0}_{1}".format(
                  kernel_size, layer_num + 1
              ),
          )
          stride = 1
      net = activation_fn(net)
      net = slim.separable_conv2d(
          net,
          filter_size,
          kernel_size,
          depth_multiplier=1,
          scope="separable_{0}x{0}_{1}".format(kernel_size, num_layers),
          stride=stride,
      )
      net = slim.batch_norm(
          net,
          scope="bn_sep_{0}x{0}_{1}".format(kernel_size, num_layers),
      )
      return net
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling a global function with example usage: _stacked_separable_conv(net, stride, operation, filter_size, use_bounded_activation) and returns: net