Hello, I’m new to PyTorch! And I have seen the power of autograd.I know for Tensors in PyTorch, the deault requires_grad values is false.So if you wanna use autograd ,you have got to explicitly specify requires_grad to be True. But when I use the model built in torch.nn.Sequential or torch.nn.Modules subclasses, I find that without specifying , the optimizer can also work fine! Anyone can help me , thanks！

Just like this:

```
# _*_ coding: utf-8 _*_
import random
import torch
# An example to show the dynamic features of PyTorch
class DynamicNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(DynamicNet, self).__init__()
self.input_linear = torch.nn.Linear(D_in, H)
self.middle_linear = torch.nn.Linear(H, H)
self.output_linear = torch.nn.Linear(H, D_out)
def forward(self, x):
'''
For the forward pass of the model, we randomly choose either 0,1,2 or 3
and reuse the middle_linear Module that many times to compute hidden
layer representation
:param x:
:return:
'''
h_relu = self.input_linear(x).clamp(min=0)
for _ in range(random.randint(0, 3)):
h_relu = self.middle_linear(h_relu).clamp(min=0)
y_pred = self.output_linear(h_relu)
return y_pred
if __name__ == '__main__':
N, D_in, H, D_out = 64, 1000, 100, 10
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
model = DynamicNet(D_in, H, D_out)
loss_fn = torch.nn.MSELoss(reduction='sum')
# todo: why it works fine without setting requires_grad = True
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
EPOCH = 500
for t in range(EPOCH):
y_pred = model(x)
loss = loss_fn(y_pred, y)
print(t, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
```