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