code: def _build_network(
      self,
      inputs,
      datas=None,
      n_stacks=1,
      n_channels=FLAGS["n_landmarks"],
      is_training=True,
  ):
      # gt_heatmap, gt_lms, mask_index, gt_mask = datas
  
      batch_size = tf.shape(inputs)[0]
      height = tf.shape(inputs)[1]
      width = tf.shape(inputs)[2]
  
      net = inputs
  
      # net = models.StackedHourglass(net, FLAGS.n_landmarks)
      # states.append(net)
      # net = tf.stop_gradient(net)
      # net *= gt_mask[:,None,None,:]
      # net = tf.concat([inputs,net], 3)
      # net = models.StackedHourglass(net, FLAGS.n_landmarks)
      # states.append(net)
  
      batch_size = tf.shape(inputs)[0]
      height = tf.shape(inputs)[1]
      width = tf.shape(inputs)[2]
      channels = tf.shape(inputs)[3]
  
      states = []
  
      with slim.arg_scope(
          [slim.batch_norm, slim.layers.dropout],
          is_training=is_training,
      ):
          with slim.arg_scope(models.hourglass_arg_scope_tf()):
              net = None
              # stacked hourglass
              for i in range(n_stacks):
                  with tf.variable_scope("stack_%02d" % i):
                      if net is not None:
                          net = tf.concat((inputs, net), 3)
                      else:
                          net = inputs
  
                      net, _ = models.hourglass(
                          net,
                          regression_channels=n_channels,
                          classification_channels=0,
                          deconv="transpose",
                          bottleneck="bottleneck_inception",
                      )
  
                      states.append(net)
  
              prediction = net
              return prediction, states
  
file_path: insightface/reconstruction/ostec/external/landmark_detector/networks.py
imports: import tensorflow
  from external.landmark_detector import utils, models, data_provider
  from tensorflow.python.platform import tf_logging
  from external.landmark_detector.flags import FLAGS
label_desc: Use image node_id 3 for calling the DNFaceMultiView obj's underlying member method code with example usage: obj._build_network(inputs, datas, n_stacks, n_channels, is_training) and returns: prediction,  states