code: def get_mlp():
      
      data = mx.symbol.Variable('data')
      fc1 = mx.symbol.CaffeOp(data_0=data, num_weight=2, name='fc1',
                              prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 128} }")
      act1 = mx.symbol.CaffeOp(data_0=fc1, prototxt="layer{type:\"TanH\"}")
      fc2 = mx.symbol.CaffeOp(data_0=act1, num_weight=2, name='fc2',
                              prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 64} }")
      act2 = mx.symbol.CaffeOp(data_0=fc2, prototxt="layer{type:\"TanH\"}")
      fc3 = mx.symbol.CaffeOp(data_0=act2, num_weight=2, name='fc3',
                              prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 10}}")
      if use_caffe_loss:
          label = mx.symbol.Variable('softmax_label')
          mlp = mx.symbol.CaffeLoss(data=fc3, label=label, grad_scale=1, name='softmax',
                                    prototxt="layer{type:\"SoftmaxWithLoss\"}")
      else:
          mlp = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')
      return mlp
docs: Get multi-layer perceptron