import tensorflow as tf

                conv1 = conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
                net.append(conv1)
      
        with tf.variable_scope('branch2_3x3'):
            if o2s1>0:
                conv3a = conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
                conv3 = conv(conv3a, o2s1, o2s2, 3, 3, ks, ks, 'SAME', 'conv3x3', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
                net.append(conv3)
      
        with tf.variable_scope('branch3_5x5'):
            if o3s1>0:
                conv5a = conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
                conv5 = conv(conv5a, o3s1, o3s2, 5, 5, ks, ks, 'SAME', 'conv5x5', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
                net.append(conv5)
      
        with tf.variable_scope('branch4_pool'):
            if poolType=='MAX':
                pool = mpool(inp, o4s1, o4s1, o4s3, o4s3, 'SAME', 'pool')
