  We introduce Mahakala, a Python-based, modular, radiative ray-tracing code
for curved space-times. We employ Google's JAX framework for accelerated
automatic differentiation, which can efficiently compute Christoffel symbols
directly from the metric, allowing the user to easily and quickly simulate
photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run
in parallel on both CPUs and GPUs and achieve speeds comparable to C-based
codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system,
which avoids numerical issues caused by the "pole" of spherical coordinates. We
demonstrate Mahakala's capabilities by simulating the 1.3 mm wavelength images
(the wavelength of Event Horizon Telescope observations) of general
relativistic magnetohydrodynamic simulations of low-accretion rate supermassive
black holes. The modular nature of Mahakala allows us to easily quantify the
relative contribution of different regions of the flow to image features. We
show that most of the emission seen in 1.3 mm images originates close to the
black hole. We also quantify the relative contribution of the disk, forward
jet, and counter jet to 1.3 mm images.
