I want to give every parameter a probability to change like this

```
for loop in range(10):
for image, label in correctSet:
image = image.to(device)
label = label.to(device)
image = Variable(image)
layerName = list(model.state_dict().keys())
# loop times is the number of layers
for i in range(len(layerName)):
layerWeight = model.state_dict()[layerName[i]].clone()
weightIndex = model.state_dict()[layerName[i]].shape
# the number of for-loop in the below
forNumber = len(weightIndex)
if forNumber == 1:
for k0 in range(weightIndex[0]):
for m in range(32):
if random.random() < faultProbability:
model.state_dict()[layerName[i]][k0] \
= bitFlip(layerWeight[k0], m)
if forNumber == 2:
for k0 in range(weightIndex[0]):
for k1 in range(weightIndex[1]):
for m in range(32):
if random.random() < faultProbability:
model.state_dict()[layerName[i]][k0][k1] \
= bitFlip(layerWeight[k0][k1], m)
if forNumber == 4:
for k0 in range(weightIndex[0]):
for k1 in range(weigge(weightIndex[2]):
for khtIndex[1]):
for k2 in ran3 in range(weightIndex[3]):
for m in range(32):
if random.random() < faultProbability:
model.state_dict()[layerName[i]][k0][k1][k2][k3] \
= bitFlip(layerWeight[k0][k1][k2][k3], m)
```

But the problem is that every time when changing a different structure model, the code have to change. So I want to know whether there exist a method to iterate the parameter of model linearly.