nnrl/nn/distributions/abstract.py
def deterministic(self, params: Dict[str, torch.Tensor]):
        """
        Generates a deterministic sample or batch of samples if the distribution
        parameters are batched. Returns a (rsample, log_prob) pair.
        """
        if self.distribution is not None:
            return self.distribution.deterministic()
        return torch.tensor(np.nan).float(), torch.tensor(np.nan).float()

        Generates a deterministic sample or batch of samples if the distribution
        parameters are batched. Returns a (rsample, log_prob) pair.
        
Generates a deterministic sample or batch of samples if the distribution
parameters are batched. Returns a (rsample, log_prob) pair.
Generates a deterministic sample or batch of samples if the distribution parameters are batched . Returns a ( rsample log_prob ) pair .
def deterministic(self, params: Dict[str, torch.Tensor]):
        if self.distribution is not None:
            return self.distribution.deterministic()
        return torch.tensor(np.nan).float(), torch.tensor(np.nan).float()
def deterministic ( self , params : Dict [ str , torch . Tensor ] ) : if self . distribution is not None : return self . distribution . deterministic ( ) return torch . tensor ( np . nan ) . float ( ) , torch . tensor ( np . nan ) . float ( )
Generates a deterministic sample or batch of samples if the distribution
parameters are batched.
Generates a deterministic sample or batch of samples if the distribution parameters are batched .
"""
        Generates a deterministic sample or batch of samples if the distribution
        parameters are batched. Returns a (rsample, log_prob) pair.
        """
self
params
Dict[str, torch.Tensor]