def step(self, vector_action=None, memory=None, text_action=None, value=None, custom_action=None) -> AllBrainInfo:
        """
        Provides the environment with an action, moves the environment dynamics forward accordingly,
        and returns observation, state, and reward information to the agent.
        :param value: Value estimates provided by agents.
        :param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.
        :param memory: Vector corresponding to memory used for recurrent policies.
        :param text_action: Text action to send to environment for.
        :param custom_action: Optional instance of a CustomAction protobuf message.
        :return: AllBrainInfo  : A Data structure corresponding to the new state of the environment.
        """
        vector_action = {} if vector_action is None else vector_action
        memory = {} if memory is None else memory
        text_action = {} if text_action is None else text_action
        value = {} if value is None else value
        custom_action = {} if custom_action is None else custom_action

        # Check that environment is loaded, and episode is currently running.
        if self._loaded and not self._global_done and self._global_done is not None:
            if isinstance(vector_action, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    vector_action = {self._external_brain_names[0]: vector_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names a keys, "
                        "and vector_actions as values".format(self._num_brains))
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a vector_action input")

            if isinstance(memory, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    memory = {self._external_brain_names[0]: memory}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and memories as values".format(self._num_brains))
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a memory input")

            if isinstance(text_action, self.SINGLE_BRAIN_TEXT_TYPES):
                if self._num_external_brains == 1:
                    text_action = {self._external_brain_names[0]: text_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and text_actions as values".format(self._num_brains))
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a value input")

            if isinstance(value, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    value = {self._external_brain_names[0]: value}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and state/action value estimates as values".format(self._num_brains))
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a value input")

            if isinstance(custom_action, CustomAction):
                if self._num_external_brains == 1:
                    custom_action = {self._external_brain_names[0]: custom_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and CustomAction instances as values".format(self._num_brains))
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a custom_action input")

            for brain_name in list(vector_action.keys()) + list(memory.keys()) + list(
                    text_action.keys()):
                if brain_name not in self._external_brain_names:
                    raise UnityActionException(
                        "The name {0} does not correspond to an external brain "
                        "in the environment".format(brain_name))

            for brain_name in self._external_brain_names:
                n_agent = self._n_agents[brain_name]
                if brain_name not in vector_action:
                    if self._brains[brain_name].vector_action_space_type == "discrete":
                        vector_action[brain_name] = [0.0] * n_agent * len(
                            self._brains[brain_name].vector_action_space_size)
                    else:
                        vector_action[brain_name] = [0.0] * n_agent * \
                                                    self._brains[
                                                        brain_name].vector_action_space_size[0]
                else:
                    vector_action[brain_name] = self._flatten(vector_action[brain_name])
                if brain_name not in memory:
                    memory[brain_name] = []
                else:
                    if memory[brain_name] is None:
                        memory[brain_name] = []
                    else:
                        memory[brain_name] = self._flatten(memory[brain_name])
                if brain_name not in text_action:
                    text_action[brain_name] = [""] * n_agent
                else:
                    if text_action[brain_name] is None:
                        text_action[brain_name] = [""] * n_agent
                    if isinstance(text_action[brain_name], str):
                        text_action[brain_name] = [text_action[brain_name]] * n_agent
                if brain_name not in custom_action:
                    custom_action[brain_name] = [None] * n_agent
                else:
                    if custom_action[brain_name] is None:
                        custom_action[brain_name] = [None] * n_agent
                    if isinstance(custom_action[brain_name], CustomAction):
                        custom_action[brain_name] = [custom_action[brain_name]] * n_agent

                number_text_actions = len(text_action[brain_name])
                if not ((number_text_actions == n_agent) or number_text_actions == 0):
                    raise UnityActionException(
                        "There was a mismatch between the provided text_action and "
                        "the environment's expectation: "
                        "The brain {0} expected {1} text_action but was given {2}".format(
                            brain_name, n_agent, number_text_actions))

                discrete_check = self._brains[brain_name].vector_action_space_type == "discrete"

                expected_discrete_size = n_agent * len(
                    self._brains[brain_name].vector_action_space_size)

                continuous_check = self._brains[brain_name].vector_action_space_type == "continuous"

                expected_continuous_size = self._brains[brain_name].vector_action_space_size[
                                               0] * n_agent

                if not ((discrete_check and len(
                        vector_action[brain_name]) == expected_discrete_size) or
                        (continuous_check and len(
                            vector_action[brain_name]) == expected_continuous_size)):
                    raise UnityActionException(
                        "There was a mismatch between the provided action and "
                        "the environment's expectation: "
                        "The brain {0} expected {1} {2} action(s), but was provided: {3}"
                            .format(brain_name, str(expected_discrete_size)
                        if discrete_check
                        else str(expected_continuous_size),
                                    self._brains[brain_name].vector_action_space_type,
                                    str(vector_action[brain_name])))

            outputs = self.communicator.exchange(
                self._generate_step_input(vector_action, memory, text_action, value, custom_action))
            if outputs is None:
                raise KeyboardInterrupt
            rl_output = outputs.rl_output
            state = self._get_state(rl_output)
            self._global_done = state[1]
            for _b in self._external_brain_names:
                self._n_agents[_b] = len(state[0][_b].agents)
            return state[0]
        elif not self._loaded:
            raise UnityEnvironmentException("No Unity environment is loaded.")
        elif self._global_done:
            raise UnityActionException(
                "The episode is completed. Reset the environment with 'reset()'")
        elif self.global_done is None:
            raise UnityActionException(
                "You cannot conduct step without first calling reset. "
                "Reset the environment with 'reset()'")
Provides the environment with an action, moves the environment dynamics forward accordingly,
        and returns observation, state, and reward information to the agent.
        :param value: Value estimates provided by agents.
        :param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.
        :param memory: Vector corresponding to memory used for recurrent policies.
        :param text_action: Text action to send to environment for.
        :param custom_action: Optional instance of a CustomAction protobuf message.
        :return: AllBrainInfo  : A Data structure corresponding to the new state of the environment.