nnrl/nn/model/stochastic/single.py
def rsample(self, params: TensorDict, sample_shape: List[int] = ()) -> SampleLogp:
        """
        Generates a sample_shape shaped reparameterized sample or sample_shape
        shaped batch of reparameterized samples if the distribution parameters
        are batched. Returns a (rsample, log_prob) pair.
        """
        return self.dist.rsample(params, sample_shape)

        Generates a sample_shape shaped reparameterized sample or sample_shape
        shaped batch of reparameterized samples if the distribution parameters
        are batched. Returns a (rsample, log_prob) pair.
        
Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched. Returns a (rsample, log_prob) pair.
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched . Returns a ( rsample log_prob ) pair .
def rsample(self, params: TensorDict, sample_shape: List[int] = ()) -> SampleLogp:
        return self.dist.rsample(params, sample_shape)
def rsample ( self , params : TensorDict , sample_shape : List [ int ] = ( ) ) -> SampleLogp : return self . dist . rsample ( params , sample_shape )
Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched.
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched .
"""
        Generates a sample_shape shaped reparameterized sample or sample_shape
        shaped batch of reparameterized samples if the distribution parameters
        are batched. Returns a (rsample, log_prob) pair.
        """
self
params
TensorDict
sample_shape
List[int]