nnrl/nn/model/stochastic/ensemble.py
def rsample(self, params: List[TensorDict]) -> List[SampleLogp]:
        """Compute reparameterized samples and likelihoods for each model.

        Uses the same semantics as :meth:`SME.sample`.
        """
        return [m.rsample(params[i]) for i, m in enumerate(self)]
Compute reparameterized samples and likelihoods for each model.

        Uses the same semantics as :meth:`SME.sample`.
        
Compute reparameterized samples and likelihoods for each model.
Uses the same semantics as :meth:`SME.sample`.
Compute reparameterized samples and likelihoods for each model . Uses the same semantics as : meth : ` SME . sample ` .
def rsample(self, params: List[TensorDict]) -> List[SampleLogp]:
        return [m.rsample(params[i]) for i, m in enumerate(self)]
def rsample ( self , params : List [ TensorDict ] ) -> List [ SampleLogp ] : return [ m . rsample ( params [ i ] ) for i , m in enumerate ( self ) ]
Compute reparameterized samples and likelihoods for each model.
Compute reparameterized samples and likelihoods for each model .
"""Compute reparameterized samples and likelihoods for each model.

        Uses the same semantics as :meth:`SME.sample`.
        """
self
params
List[TensorDict]