nnrl/nn/model/stochastic/single.py
def sample(self, params: TensorDict, sample_shape: List[int] = ()) -> SampleLogp:
        """
        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.
        """
        return self.dist.sample(params, sample_shape)

        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, params: TensorDict, sample_shape: List[int] = ()) -> SampleLogp:
        return self.dist.sample(params, sample_shape)
def sample ( self , params : TensorDict , sample_shape : List [ int ] = ( ) ) -> SampleLogp : return self . dist . sample ( params , sample_shape )
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
params
TensorDict
sample_shape
List[int]