I am trying to get the hessian after a model has completed estimating (to then use to calculate standard errors). Given a model object, my thought was that something like the following should work:

```
d1 = torch.autograd.grad(loss, model.parameters(), create_graph=True)[0]
d2 = torch.autograd.grad(loss, d1, create_graph=True)[0]
```

However, this gives the error “One of the differentiated Tensors appears to not have been used in the graph.” Could someone point me in the right direction?

The model class, for what it’s worth, is the following:

```
class MultinomialLogitWithEmbeddings(torch.nn.Module):
def __init__(self, m, n_fe):
super(MultinomialLogitWithEmbeddings,self).__init__()
self.n_features = m
self.n_fe_cats = n_fe
self.emb_dim = 1
# Layers
self.emb = torch.nn.Embedding(self.n_fe_cats, self.emb_dim) # Store FEs as an embeddings lookup
self.linear = torch.nn.Linear(self.n_features + self.emb_dim, 1, bias=False) # Linear utility layer
self.lsmax = torch.nn.LogSoftmax(1) # Log soft max activation
# Initialize the layer weights
nn.init.kaiming_normal_(self.linear.weight.data)
def forward(self, x_cont, x_cat):
x = torch.cat([x_cont.float(), self.emb(x_cat).squeeze(dim=-1).float()], 2)
y_pred=self.linear(x)
y_pred=self.lsmax(y_pred)
return y_pred.squeeze()
```