parameters: (self)
docstring: Constructs a Variable-parameterized prior mean.
  
      Models should wrap any variables defined here in the model's variable scope.
  
      Returns:
        A one-dimensional floating point Tensor with shape [state dimension]
        indicating the prior mean.
docstring_summary: Constructs a Variable-parameterized prior mean.
docstring_tokens: Constructs a Variable - parameterized prior mean .
function: def get_prior_mean(self):
      """Constructs a Variable-parameterized prior mean.
  
      Models should wrap any variables defined here in the model's variable scope.
  
      Returns:
        A one-dimensional floating point Tensor with shape [state dimension]
        indicating the prior mean.
      """
      with variable_scope.variable_scope(self._variable_scope):
        state_transition = ops.convert_to_tensor(
            self.get_state_transition(), dtype=self.dtype)
        state_dimension = state_transition.get_shape()[0].value
        return variable_scope.get_variable(
            name="prior_state_mean",
            shape=[state_dimension],
            dtype=self.dtype,
            trainable=self._configuration.trainable_start_state)
function_tokens: def get_prior_mean ( self ) : with variable_scope . variable_scope ( self . _variable_scope ) : state_transition = ops . convert_to_tensor ( self . get_state_transition ( ) , dtype = self . dtype ) state_dimension = state_transition . get_shape ( ) [ 0 ] . value return variable_scope . get_variable ( name = "prior_state_mean" , shape = [ state_dimension ] , dtype = self . dtype , trainable = self . _configuration . trainable_start_state )