nnrl/nn/distributions/abstract.py
def log_prob(self, value):
        """
        Returns the log of the probability density/mass function evaluated at `value`.
        """
        if self.cond_dist is not None:
            return self.cond_dist.log_prob(value, self.params)
        return torch.tensor(np.nan).float().expand_as(value)

        Returns the log of the probability density/mass function evaluated at `value`.
        
Returns the log of the probability density/mass function evaluated at `value`.
Returns the log of the probability density / mass function evaluated at ` value ` .
def log_prob(self, value):
        if self.cond_dist is not None:
            return self.cond_dist.log_prob(value, self.params)
        return torch.tensor(np.nan).float().expand_as(value)
def log_prob ( self , value ) : if self . cond_dist is not None : return self . cond_dist . log_prob ( value , self . params ) return torch . tensor ( np . nan ) . float ( ) . expand_as ( value )
Returns the log of the probability density/mass function evaluated at `value`.
Returns the log of the probability density / mass function evaluated at ` value ` .
"""
        Returns the log of the probability density/mass function evaluated at `value`.
        """
self
value