Weak convergence of a sequence probability measures, denoted $P_n \Rightarrow P$, is typically defined as,
$$ \int\limits_{\mathbb{R}}fdP_n \xrightarrow{n\rightarrow\infty} \int\limits_{\mathbb{R}}fdP \;\;\;\; (*)$$
for all $f \in C_b(\mathbb{R})$, the space of continuous and bounded functions. However I read here that if $(*)$ holds for all $f \in C_c^\infty(\mathbb{R})$, the space of smooth compactly supported functions, then weak convergence also holds. The proof in the link is as follows:
Fix $\epsilon > 0$ and choose a smooth compactly supported function $g$ with $0 \leq g \leq 1$ and $\int gdP \geq 1−ϵ$. Let $K$ be the support of $g$. Then by assumption, $$\int gdP_n\rightarrow\int gdP \geq 1 - \epsilon$$ so we see that, $P_n(K)\geq 1−\epsilon$. In other words, $\{P_n\}$ is tight. Hence after passing to a subsequence, $\{P_{n_k}\}$ converges weakly to some measure $P^*$. Now we notice that $\int fdP =\int fdP^*$ for all $f\in C_c^\infty(\mathbb{R})$, and it follows from a monotone class type argument that $P = P^*$. Finally use the "double subsequence" trick to conclude that the original sequence $\{P_n\}$ also converges weakly to $P$.
I think I am able to follow the above until the last sentence. How we can conclude weak convergence of the original sequence? All we've shown is that for all $f \in C_b(\mathbb{R})$,
$$ \int f dP_{n_k} \xrightarrow{k\rightarrow\infty} \int f dP $$
for this particular subsequence $\{P_{n_k}\}$. I'm not sure what the "double subsequence" trick is. Is it some sort of diagonal argument? How can we use it to conclude that,
$$ \int\limits_{\mathbb{R}}fdP_n \xrightarrow{n\rightarrow\infty} \int\limits_{\mathbb{R}}fdP $$
for all $f \in C_b(\mathbb{R})$?