*m, 
                  torch.quantization.DeQuantStub())
"""Prepare"""
m.train()
m.qconfig = torch.quantization.get_default_qconfig(backend)
torch.quantization.prepare_qat(m, inplace=True)
"""Training Loop"""
n_epochs = 10
opt = torch.optim.SGD(m.parameters(), lr=0.1)
loss_fn = lambda out, tgt: torch.pow(tgt-out, 2).mean()
for epoch in range(n_epochs):
  x = torch.rand(10,2,24,24)
  out = m(x)
  loss = loss_fn(out, torch.rand_like(out))
  opt.zero_grad()
  loss.backward()
  opt.step()
"""Convert"""
m.eval()
torch.quantization.convert(m, inplace=True)
```
Sensitivity Analysis