Why does by torch.optim.SGD method learning rate change?

With SGD learning rate should not be changed during epochs but it is. Help me understand why it happens please and how to prevent this LR changing?

import torch
params = [torch.nn.Parameter(torch.randn(1, 1))]
optimizer = torch.optim.SGD(params, lr=0.9)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)
for epoch in range(5):
    print(scheduler.get_lr())
    scheduler.step()

Output is:

[0.9]
[0.7290000000000001]
[0.6561000000000001]
[0.5904900000000002]
[0.5314410000000002]

My torch version is 1.4.0

To expand upon xiawi's answer about "strange" behavior (0.81 is missing): It is PyTorch's default way since 1.1.0 release, check documentation, namely this part:

[...] If you use the learning rate scheduler (calling scheduler.step()) before the optimizer’s update (calling optimizer.step()), this will skip the first value of the learning rate schedule.

Additionally you should get a UserWarning thrown by this function after the first get_lr() call as you do not call optimizer.step() at all.