new_contents: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  
  # This example demonstrates how to manipulate the learning rate of an optimizer
  # in gluon. The example uses linear regression as a case study.
  
  from __future__ import print_function
  import numpy as np
  import mxnet as mx
  from mxnet import autograd
  from mxnet import gluon
  
  # Generate synthetic data.
  X = np.random.randn(10000, 2)
  Y = 2 * X[:, 0] - 3.4 * X[:, 1] + 4.2 + .01 * np.random.normal(size=10000)
  
  net = gluon.nn.Sequential()
  # The output dimension is 1.
  net.add(gluon.nn.Dense(1))
  net.collect_params().initialize()
  loss = gluon.loss.L2Loss()
  
  # Initialize the learning rate as 0.1.
  trainer = gluon.Trainer(net.collect_params(), 'sgd',
                          optimizer_params={'learning_rate': 0.1})
  net.collect_params().initialize(mx.init.Xavier(magnitude=2.24),
                                  force_reinit=True)
  train_data = mx.io.NDArrayIter(X, Y, batch_size=10, shuffle=True)
  
  for epoch in range(5):
      train_data.reset()
      for i, batch in enumerate(train_data):
          data = batch.data[0]
          label = batch.label[0].reshape((-1, 1))
          with autograd.record():
              output = net(data)
              mse = loss(output, label)
          mse.backward()
          trainer.step(data.shape[0])
      # After the second epoch, decay the learning rate of the optimizer every
      # epoch.
      if epoch > 1:
          trainer.set_learning_rate(trainer.learning_rate * 0.9)
      print('Epoch:', epoch, 'Learning rate:', trainer.learning_rate)
  
  for para_name, para_value in net.collect_params().items():
      # Print all the parameter values after training.
      print(para_name, para_value.data().asnumpy()[0])
subject: Add example of manipulating learning rates in gluon (#7834)