How to prove $\mathcal{I}_{1}(\eta) = \mathcal{I}_{1}(\theta)[h'(\eta)]^{2}$ where $\mathcal{I}_{1}$ is the Fisher information and $\theta = h(\eta)$? I am trying to apply the following definition of Fisher Information:
\begin{align*}
\mathcal{I}_{1}(\theta) = \mathbb{E}_{\theta}\left[\left(\frac{\partial}{\partial\theta}\ln f(x_{1}|\theta)\right)^{2}\right]
\end{align*}
But I have not succeeded so far. This is my attempt so far:
\begin{align*}
\mathcal{I}_{1}(\eta) & = \mathbb{E}_{\eta}\left[\left(\frac{\partial}{\partial\eta}\ln f(x_{1}|\eta)\right)^{2}\right]\\\\
& = \mathbb{E}_{\eta}\left[\left(\frac{\partial\theta}{\partial\eta}\frac{\partial}{\partial\theta}\ln f(x_{1}|\eta)\right)^{2}\right]
\end{align*}
Can someone help me to finish the demonstration?

Start out from likelihood function $L(\theta;Y_1)$ (for a single observation) and make the reparametrization $\theta = h(\eta)$ to get the reparametrized log-likelihood $L(\eta;Y_1) = L(h(\eta);Y_1)$. Then the log-likelihood function of $\eta$ is $\ell(\eta;Y_1) = \ell(h(\eta);Y_1)$ so the Fisher information for a single observation is
\begin{align*}
I_1(\eta) & = E_\theta\left[\left(\frac{d \ell(\eta;Y_1)}{d\eta}\right)^2\right]\\
& = E_\theta\left[\left(\frac{d \ell(h(\eta;Y_1))}{d h(\eta)}\frac{dh(\eta)}{d\eta}\right)^2\right]\\
& = E_\theta\left[\left(\frac{d \ell(\theta;Y_1)}{d \theta}\right)^2\right]\left(\frac{dh(\eta)}{d\eta}\right)^2\\
& = I_1(\theta)\left(\frac{dh(\eta)}{d\eta}\right)^2.
\end{align*}
