level_replay/envs.py [135:148]:
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        if self.level_sampler:
            return obs, seeds
        else:
            return obs

    def step_async(self, actions):
        if isinstance(actions, torch.LongTensor) or len(actions.shape) > 1:
            # Squeeze the dimension for discrete actions
            actions = actions.squeeze(1)
        actions = actions.cpu().numpy()
        self.venv.step_async(actions)

    def step_wait(self):
        obs, reward, done, info = self.venv.step_wait()
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level_replay/envs.py [226:239]:
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        if self.level_sampler:
            return obs, seeds
        else:
            return obs

    def step_async(self, actions):
        if isinstance(actions, torch.LongTensor) or len(actions.shape) > 1:
            # Squeeze the dimension for discrete actions
            actions = actions.squeeze(1)
        actions = actions.cpu().numpy()
        self.venv.step_async(actions)

    def step_wait(self):
        obs, reward, done, info = self.venv.step_wait()
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