>>> import paddle

>>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
>>> linear = paddle.nn.Linear(10, 10)
>>> inp = paddle.to_tensor(inp)
>>> out = linear(inp)
>>> loss = paddle.mean(out)
>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
>>> beta2 = paddle.to_tensor([0.99], dtype="float32")
>>> momentum = paddle.optimizer.Momentum(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
>>> back = out.backward()
>>> momentum.step()
>>> momentum.clear_grad()

>>> # Note that the learning_rate of linear_2 is 0.01.
>>> linear_1 = paddle.nn.Linear(10, 10)
>>> linear_2 = paddle.nn.Linear(10, 10)
>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
>>> out = linear_1(inp)
>>> out = linear_2(out)
>>> loss = paddle.mean(out)
>>> momentum = paddle.optimizer.Momentum(
...     learning_rate=0.1,
...     parameters=[{
...         'params': linear_1.parameters()
...     }, {
...         'params': linear_2.parameters(),
...         'weight_decay': 0.001,
...         'learning_rate': 0.1
...     }],
...     weight_decay=0.01,
...     momentum=0.9
... )
>>> out.backward()
>>> momentum.step()
>>> momentum.clear_grad()