class call_option_MC(torch.nn.Module):

def **init**(self, nb_steps):

super(call_option_MC, self).**init**()

“”"

torch.set_num_threads(2)

“”"

self.nb_steps = nb_steps

self.sqrt_dt = torch.sqrt(torch.tensor(1 / torch.randn(self.nb_steps).shape[0]))

```
#Return train set X, y, Z
def train_set(self, states):
initial_price = states[:, 0]
strike = states[:, 1]
time_to_expiry = states[:, 2]
implied_vol = states[:, 3]
risk_free_rate = states[:, 4]
dW = self.sqrt_dt * torch.randn(self.nb_steps * states.shape[0]).reshape(states.shape[0], self.nb_steps)
implied_vol = states[:, 3]
path = states[:, 0].clone()
path_list = []
for step in range(self.nb_steps):
path += path * dW[:, step] * implied_vol
path_list.append(path.clone())
final_price = path_list[-1]
discount_factor = torch.exp(-risk_free_rate * time_to_expiry)
option_payoff = torch.max(final_price - strike, torch.tensor(0.0))
option_price = discount_factor * option_payoff
return states, option_price
```

Hi everyone, I am new here and would need some help. I have this code above and would like to get a variable derivatives of dimension (states[0], states[1]) where the element (i,j) is the derivatives of option_price[i] with respect to states[i,j]

I tried using backward or autograd but keep getting errors …