Why is the stationary distribution a distribution?

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Suppose we have a time-homogeneous, discrete-time, aperiodic, positive recurrent, irreducible Markov chain $(X_t)_{t \geq 0}$ on a discrete state space $E$. It is known that its stationary distribution is given by $$\pi(x)=\frac{1}{E_x[H_x]}, \quad x \in E,$$ where $H_x$ is the first positive time of visiting $x$ when starting at $x$, i.e.

$$H_x:=\inf\{t \geq 1 \colon X_t= x\}.$$

Why do we know that it is a distribution, so how can one formally see that

$$\sum_{x \in E} \frac{1}{E_x[H_x]}=1?$$

I know about the intuition, but I would really like to see a very formal proof of it or at least a hint how to prove it very formally.