Eating is a crucial activity of daily living. Unfortunately, for the millions of people who cannot eat independently due to a disability, caregiver-assisted meals can come with feelings of self-consciousness, pressure, and being a burden. Robot-assisted feeding promises to empower people with motor impairments to feed themselves. However, research often focuses on specific system subcomponents and thus evaluates them in controlled settings. This leaves a gap in developing and evaluating an end-to-end system that feeds users entire meals in out-of-lab settings. We present such a system, collaboratively developed with community researchers. The key challenge of developing a robot feeding system for out-of-lab use is the varied off-nominal scenarios that can arise. Our key insight is that users can be empowered to overcome many off-nominals, provided customizability and control. This system improves upon the state-of-the-art with: (a) a user interface that provides substantial customizability and control; (b) general food detection; and (c) portable hardware. We evaluate the system with two studies. In Study 1, 5 users with motor impairments and 1 community researcher use the system to feed themselves meals of their choice in a cafeteria, office, or conference room. In Study 2, 1 community researcher uses the system in his home for 5 days, feeding himself 10 meals across diverse contexts. This resulted in 3 lessons learned: (a) spatial contexts are numerous, customizability lets users adapt to them; (b) off-nominals will arise, variable autonomy lets users overcome them; (c) assistive robots' benefits depend on context.