code: def conv_bn_layer(
      input,
      ch_out,
      filter_size,
      stride,
      padding,
      active_type=paddle.activation.Relu(),
      ch_in=None,
  ):
      tmp = paddle.layer.img_conv(
          input=input,
          filter_size=filter_size,
          num_channels=ch_in,
          num_filters=ch_out,
          stride=stride,
          padding=padding,
          act=paddle.activation.Linear(),
          bias_attr=False,
      )
      return paddle.layer.batch_norm(input=tmp, act=active_type)
  
file_path: MMdnn/mmdnn/conversion/examples/paddle/models/resnet.py
imports: import paddle.v2
label_desc: Use image node_id 1 for calling a global function with example usage: conv_bn_layer(input, ch_out, filter_size, stride, padding, active_type, ch_in) and returns: paddle