The model is here:
class Actor(nn.Module):
    def __init__(self, state_size, action_size, hidden_size=512):
        super(Actor, self).__init__()
        
        self.state_size = state_size
        self.hidden_size = hidden_size
        self.action_size = action_size
        
        self.block_state = nn.Sequential(
            nn.Linear(state_size, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(),
        )
        
        self.block_hidden = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(),
        )
        
        self.block_mean = nn.Sequential(
            nn.Linear(hidden_size, action_size),
        )
        
        self.block_std = nn.Sequential(
            nn.Linear(hidden_size, action_size),
        )
        
    def forward(self, state):
        out = self.block_state(state)
        out = self.block_hidden(out)
        mean = self.block_mean(out)
        std = self.block_std(out)
        return mean,std

The output is:
tensor([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan]], grad_fn=)
tensor([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan]], grad_fn=)
I’m sure the input doesn’t contain any nan value.