nnrl/nn/distributions/abstract.py
def reproduce(self, value: torch.Tensor, params: Dict[str, torch.Tensor]):
        """Produce a reparametrized sample with the same value as `value`."""
        if self.distribution is not None:
            return self.distribution.reproduce(value)
        return (
            torch.tensor(np.nan).float().expand_as(value),
            torch.tensor(np.nan).float().expand_as(value),
        )
Produce a reparametrized sample with the same value as `value`.
Produce a reparametrized sample with the same value as `value`.
Produce a reparametrized sample with the same value as ` value ` .
def reproduce(self, value: torch.Tensor, params: Dict[str, torch.Tensor]):
        if self.distribution is not None:
            return self.distribution.reproduce(value)
        return (
            torch.tensor(np.nan).float().expand_as(value),
            torch.tensor(np.nan).float().expand_as(value),
        )
def reproduce ( self , value : torch . Tensor , params : Dict [ str , torch . Tensor ] ) : if self . distribution is not None : return self . distribution . reproduce ( value ) return ( torch . tensor ( np . nan ) . float ( ) . expand_as ( value ) , torch . tensor ( np . nan ) . float ( ) . expand_as ( value ) , )
Produce a reparametrized sample with the same value as `value`.
Produce a reparametrized sample with the same value as ` value ` .
"""Produce a reparametrized sample with the same value as `value`."""
self
value
torch.Tensor
params
Dict[str, torch.Tensor]