original_docstring: Get the output of the instantiator model
          
          Input shape depends on the instantiator (see skrl.utils.model_instantiator.Shape) as follows:
  
          - STATES / OBSERVATIONS = 0
          - ACTIONS = -1
          - STATES_ACTIONS = -2
  
          :param states: Observation/state of the environment used to make the decision
          :type states: torch.Tensor
          :param taken_actions: Actions taken by a policy to the given states (default: None)
          :type taken_actions: torch.Tensor, optional
  
          :return: Output of the instantiator model
          :rtype: tuple of torch.Tensor
          
docstring: Get the output of the instantiator model
  Input shape depends on the instantiator  as follows.
  
  STATES / OBSERVATIONS = 0
  ACTIONS = -1
  STATES_ACTIONS = -2
docstring_tokens: Get the output of the instantiator model Input shape depends on the instantiator as follows . STATES / OBSERVATIONS = 0 ACTIONS = - 1 STATES_ACTIONS = - 2
code: def _get_instantiator_output(self, 
                                   states: torch.Tensor, 
                                   taken_actions: Union[torch.Tensor, None] = None) -> Tuple[torch.Tensor]:
          """Get the output of the instantiator model
          
          Input shape depends on the instantiator (see skrl.utils.model_instantiator.Shape) as follows:
  
          - STATES / OBSERVATIONS = 0
          - ACTIONS = -1
          - STATES_ACTIONS = -2
  
          :param states: Observation/state of the environment used to make the decision
          :type states: torch.Tensor
          :param taken_actions: Actions taken by a policy to the given states (default: None)
          :type taken_actions: torch.Tensor, optional
  
          :return: Output of the instantiator model
          :rtype: tuple of torch.Tensor
          """
          if self._instantiator_input_type == 0:
              output = self._instantiator_net(states)
          elif self._instantiator_input_type == -1:
              output = self._instantiator_net(taken_actions)
          elif self._instantiator_input_type == -2:
              output = self._instantiator_net(torch.cat((states, taken_actions), dim=1))
          
          # deterministic and categorical output
          if self._instantiator_parameter is None:
              return output * self._instantiator_output_scale
          # gaussian output
          else:
              return output * self._instantiator_output_scale, self._instantiator_parameter
code_tokens: def _get_instantiator_output ( self , states : torch . Tensor , taken_actions : Union [ torch . Tensor , None ] = None ) -> Tuple [ torch . Tensor ] : if self . _instantiator_input_type == 0 : output = self . _instantiator_net ( states ) elif self . _instantiator_input_type == - 1 : output = self . _instantiator_net ( taken_actions ) elif self . _instantiator_input_type == - 2 : output = self . _instantiator_net ( torch . cat ( ( states , taken_actions ) , dim = 1 ) ) if self . _instantiator_parameter is None : return output * self . _instantiator_output_scale else : return output * self . _instantiator_output_scale , self . _instantiator_parameter
short_docstring: Get the output of the instantiator model
  Input shape depends on the instantiator (see skrl.utils.model_instantiator.Shape) as follows:
short_docstring_tokens: Get the output of the instantiator model Input shape depends on the instantiator ( see skrl . utils . model_instantiator . Shape ) as follows :
comment: """Get the output of the instantiator model
          
          Input shape depends on the instantiator (see skrl.utils.model_instantiator.Shape) as follows:
  
          - STATES / OBSERVATIONS = 0
          - ACTIONS = -1
          - STATES_ACTIONS = -2
  
          :param states: Observation/state of the environment used to make the decision
          :type states: torch.Tensor
          :param taken_actions: Actions taken by a policy to the given states (default: None)
          :type taken_actions: torch.Tensor, optional
  
          :return: Output of the instantiator model
          :rtype: tuple of torch.Tensor
          """$SEP$# deterministic and categorical output$SEP$# gaussian output