import paddle
import numpy as np

class MyLayer(paddle.nn.Layer):
    def __init__(self, num_stacked_param):
        super(MyLayer, self).__init__()
        # create ParameterList with iterable Parameters
        self.params = paddle.nn.ParameterList(
            [paddle.create_parameter(
                shape=[2, 2], dtype='float32')] * num_stacked_param)

    def forward(self, x):
        for i, p in enumerate(self.params):
            tmp = self._helper.create_variable_for_type_inference('float32')
            self._helper.append_op(
                type="mul",
                inputs={"X": x,
                        "Y": p},
                outputs={"Out": tmp},
                attrs={"x_num_col_dims": 1,
                        "y_num_col_dims": 1})
            x = tmp
        return x

data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
x = paddle.to_tensor(data_np)
num_stacked_param = 4
model = MyLayer(num_stacked_param)
print(len(model.params))  # 4
res = model(x)
print(res.shape)  # [5, 2]

replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32')
model.params[num_stacked_param - 1] = replaced_param  # replace last param
res = model(x)
print(res.shape)  # [5, 3]
model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32'))  # append param
print(len(model.params))  # 5
res = model(x)
print(res.shape)  # [5, 4]