text: import tensorflow as tf
  import numpy as np
  import tflearn
  import sys
  import os
  import tensorflow.contrib.slim as slim
  
  BASE_DIR = os.path.dirname(os.path.abspath(__file__))
  POINTNET2_DIR = os.path.join(BASE_DIR,'pointnet4','models')
  sys.path.insert(0,POINTNET2_DIR)
  
  from pointnet2_combined_cla_all import get_model as enc_model
  
  def placeholder_inputs(batch_size, num_point, with_normal, args):
      pc_combined_pl = tf.placeholder(tf.float32, shape=(None, num_point * 2, 4), name='pc_combined_normal')
      if with_normal:
          pc_combined_normal_pl = tf.placeholder(tf.float32, shape=(None, num_point * 2, 3), name='pc_combined_normal')
      gt_succ_label_pl = tf.placeholder(tf.float32, [None, ], name='gt_succ_label')
      return pc_combined_pl, gt_succ_label_pl
  
  
  def get_model(pc_combined, pc_combined_normal=None):
      end_points = {}
      with tf.variable_scope('s3_classifier'):
          with tf.variable_scope('pointnet2'):
              if not pc_combined_normal is None:
                  pc_combined_input_feat = tf.concat([pc_combined_normal, pc_combined[:, :, 3:]], axis=-1) 
              else:
                  pc_combined_input_feat = pc_combined[:, :, 3:]  
              
              print('pc_combined_input_feat', pc_combined_input_feat)
              nets_end = enc_model(pc_combined[:, :, :3], pc_combined_input_feat, 2, is_training=True)
  
              pred_cla_score = nets_end['cla']
              print('pred_cla_score', pred_cla_score)
              end_points = nets_end
      return pred_cla_score, end_points
  
  def get_loss(pred_cla_score, gt_succ_label, end_points):
      loss_cls_ce = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.cast(gt_succ_label,dtype=tf.int32),logits=pred_cla_score))
      return loss_cls_ce
  
  if __name__ == '__main__':
      with tf.Graph().as_default():
          input_pc_combined = tf.zeros((32,4096,4))
          input_pc_combined_normal = tf.zeros((32,4096,3))
          get_model(input_pc_combined)
          