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

        Generates a sample_shape shaped sample or sample_shape shaped batch of
        samples if the distribution parameters are batched. Returns a (sample, log_prob)
        pair.
        
Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched. Returns a (sample, log_prob)
pair.
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched . Returns a ( sample log_prob ) pair .
def sample(self, sample_shape: List[int] = ()):
        if self.cond_dist is not None:
            return self.cond_dist.sample(self.params, sample_shape)
        return torch.tensor(np.nan).float(), torch.tensor(np.nan).float()
def sample ( self , sample_shape : List [ int ] = ( ) ) : if self . cond_dist is not None : return self . cond_dist . sample ( self . params , sample_shape ) return torch . tensor ( np . nan ) . float ( ) , torch . tensor ( np . nan ) . float ( )
Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched.
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched .
"""
        Generates a sample_shape shaped sample or sample_shape shaped batch of
        samples if the distribution parameters are batched. Returns a (sample, log_prob)
        pair.
        """
self
sample_shape
List[int]