text: import paddle
  from paddle import fluid
  import numpy as np
  
  paddle.enable_static()
  
  inp_blob1 = np.random.randn(1, 1, 3, 3).astype(np.float32)
  inp_blob2 = np.random.randn(1, 2, 3, 3).astype(np.float32)
  
  x1 = fluid.data(name='inputX1', shape=[1, 1, 3, 3], dtype='float32')
  x2 = fluid.data(name='inputX2', shape=[1, 2, 3, 3], dtype='float32')
  
  conv2d1 = fluid.layers.conv2d(input=x1, num_filters=1, filter_size=(1, 1), stride=(1, 1), padding=(0, 0),
                                   dilation=(1, 1), groups=1, bias_attr=False, name="conv2dX1")
  
  conv2d2 = fluid.layers.conv2d(input=x2, num_filters=1, filter_size=(1, 1), stride=(1, 1), padding=(0, 0),
                                dilation=(1, 1), groups=1, bias_attr=False, name="conv2dX2")
  
  add1 = fluid.layers.elementwise_add(conv2d1, conv2d2, name="add1")
  
  relu2a = fluid.layers.relu(add1, name="relu2a")
  relu2b = fluid.layers.relu(add1, name="relu2b")
  
  add2 = fluid.layers.elementwise_add(relu2a, relu2b, name="add2")
  
  relu3a = fluid.layers.relu(add2, name="relu3a")
  relu3b = fluid.layers.relu(add2, name="relu3b")
  
  exe = fluid.Executor(fluid.CPUPlace())
  exe.run(fluid.default_startup_program())
  inp_dict = {'inputX1': inp_blob1, 'inputX2': inp_blob2}
  var = [relu3a, relu3b]
  res_pdpd = exe.run(fluid.default_main_program(), fetch_list=var, feed=inp_dict)
  
  fluid.io.save_inference_model("../models/2in_2out", list(inp_dict.keys()), var, exe,
                                model_filename="2in_2out.pdmodel", params_filename="2in_2out.pdiparams")
  