RMSProp in PyTorch

from torch import nn
from torch import optim

class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear_layer = nn.Linear(in_features=4, out_features=5)

    def forward(self, x):
        return self.linear_layer(x)

mymodel = MyModel()

rmsprop = optim.RMSprop(params=mymodel.parameters())
rmsprop
# RMSprop (
# Parameter Group 0
#     alpha: 0.99
#     centered: False
#     differentiable: False
#     eps: 1e-08
#     foreach: None
#     lr: 0.01
#     maximize: False
#     momentum: 0
#     weight_decay: 0
# )

rmsprop.state_dict()
# {'state': {},
#  'param_groups': [{'lr': 0.01,
#    'momentum': 0,
#    'alpha': 0.99,
#    'eps': 1e-08,
#    'centered': False,
#    'weight_decay': 0,
#    'foreach': None,
#    'maximize': False,
#    'differentiable': False,
#    'params': [0, 1]}]}

rmsprop.step()
rmsprop.zero_grad()
# None
          # This is for CPU without `capturable`
rmsprop = optim.RMSprop(params=mymodel.parameters(), lr=0.01, alpha=0.99, 
                        eps=1e-08, weight_decay=0, momentum=0, 
                        centered=False, foreach=None, 
                        maximize=False, differentiable=False)
rmsprop
# RMSprop (
# Parameter Group 0
#     alpha: 0.99
#     centered: False
#     differentiable: False
#     eps: 1e-08
#     foreach: None
#     lr: 0.01
#     maximize: False
#     momentum: 0
#     weight_decay: 0
# )
          # This is for CUDA(GPU) with `capturable`
rmsprop = optim.RMSprop(params=mymodel.parameters(), lr=0.01, alpha=0.99, 
                        eps=1e-08, weight_decay=0, momentum=0, 
                        centered=False, capturable=False, foreach=None, 
                        maximize=False, differentiable=False)
rmsprop
# RMSprop (
# Parameter Group 0
#     alpha: 0.99
#     capturable: False
#     centered: False
#     differentiable: False
#     eps: 1e-08
#     foreach: None
#     lr: 0.01
#     maximize: False
#     momentum: 0
#     weight_decay: 0
# )