parameters: (self, n=1, stop_gradient=False)
docstring: Sample `n` times and concatenate along a new outer dimension.
  
      Args:
        n: A python integer or int32 tensor. The number of samples to take.
        stop_gradient: If `True`, StochasticTensors' values are wrapped in
          `stop_gradient`, to avoid backpropagation through.
docstring_summary: Sample `n` times and concatenate along a new outer dimension.
docstring_tokens: Sample n times and concatenate along a new outer dimension .
function: def __init__(self, n=1, stop_gradient=False):
      """Sample `n` times and concatenate along a new outer dimension.
  
      Args:
        n: A python integer or int32 tensor. The number of samples to take.
        stop_gradient: If `True`, StochasticTensors' values are wrapped in
          `stop_gradient`, to avoid backpropagation through.
      """
      self._n = n
      self._stop_gradient = stop_gradient
function_tokens: def __init__ ( self , n = 1 , stop_gradient = False ) : self . _n = n self . _stop_gradient = stop_gradient