I have a function $\Gamma(x): \mathbb{R}^{n} \to \mathbb{R}^{2n \times m}$ defined as $$ \Gamma(x) := \begin{bmatrix} X_{11}(x) & X_{12}(x) \\ X_{21}(x) & X_{22}(x) \end{bmatrix}^{-1} B $$ where $B \in \mathbb{R}^{2n \times m}$ is constant and $X_{ij}(x) \in \mathbb{R}^{n \times n}$. How can I find $\Gamma^{\prime}(x)$? I'm trying to use the chain rule and Cramer's rule for the adjugate but am stuck.
In particular, I define $$ X(x) := \begin{bmatrix} X_{11}(x) & X_{12}(x) \\ X_{21}(x) & X_{22}(x) \end{bmatrix} $$ so that $$\frac{{\rm d}}{{\rm d}X}\Gamma = -(X^{-1}B)^{\top} \otimes X^{-1}$$ but I am confused about pushing the chain rule through for $$\frac{{\rm d}X}{{\rm d}x}.$$
What I'm trying to do is something like: \begin{align} \frac{{\rm d}}{{\rm d} x} \Gamma &= \frac{{\rm d}\Gamma}{{\rm d}X} \cdot \frac{{\rm d}X}{{\rm d}x} \\ &= \left( -(X^{-1}B)^{\top} \otimes X^{-1} \right) \cdot \frac{{\rm d}X}{{\rm d}x} \end{align} but am unsure of what sort of product "$\cdot$" denotes here. Differentiatiating entry-by-entry of $x$ as $$ {\rm vec} \left( \frac{{\rm d}\Gamma}{{\rm d}x_i}(x) \right) = (B^{\top} \otimes I_n) \cdot {\rm vec}\left( X^{-1}(x) \cdot \frac{\partial}{\partial x_i}X(x) \cdot X^{-1}(x) \right) $$ and then stacking the resulting vectors horizontally to gives $\frac{{\rm d}}{{\rm d} x} \Gamma$, but I'm not sure how to represent this object more compactly via tensors / Krons / Frobenius inner products. Is $$ \frac{{\rm d}}{{\rm d} x} \Gamma = \left( -(X^{-1}B)^{\top} \otimes X^{-1} \right) : \frac{{\rm d}X}{{\rm d}x} $$ right? Would it be easier to use tensor notation?