linear_layer = torch.nn.Linear(1, 1)
linear_layer.weight.data += 69
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Valid, but will NOT store grad_fn=<AddBackward0>
linear_layer.weight += 420
# ^^^^^^^^^^^^^^^^^^^^^^^^
# RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.

# create a parameter predictor network (net_1)
net_1 = torch.nn.Linear(1, 2)

# predict the weights and biases of net_2 using net_1
p_weight_and_bias = net_1(input_0).unsqueeze(2)
p_weight, p_bias = p_weight_and_bias[:, 0], p_weight_and_bias[:, 1]

# create a mutable network (net_2)
net_2 = to_mutable_module(torch.nn.Linear(1, 1))

# hot-swap the weights and biases of net_2 with the predicted values
net_2.weight = p_weight
net_2.bias = p_bias

# compute the output and backpropagate the gradients to net_1
output = net_2(input_1)
loss = criterion(output, label)
loss.backward()
optimizer.step()

# import all functions
from torch_mutable_modules import *

# ... or import the function manually
from torch_mutable_modules import to_mutable_module

converted_module = to_mutable_module(
    torch.nn.Linear(1, 1)
) # type of converted_module is still torch.nn.Linear

converted_module.weight *= 0
convreted_module.weight += 69
convreted_module.weight # tensor([[69.]], grad_fn=<AddBackward0>)

class MyModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1, 1)
    
    def forward(self, x):
        return self.linear(x)

my_module = to_mutable_module(MyModule())
my_module.linear.weight *= 0
my_module.linear.weight += 69
my_module.linear.weight # tensor([[69.]], grad_fn=<AddBackward0>)

converted_module = to_mutable_module(
    torch.nn.Linear(1, 1).cuda()
) # converted_module is now a mutable module on the GPU

# both of these are valid
converted_module.weight = converted_module.weight.cuda()
converted_module.bias = converted_module.bias.to("cuda")