Operator fusion is critical for accelerating FHE-based DNN inference, as it reduces multiplicative depth and the cost of ciphertext operations. Existing approaches either rely on manual optimizations, which miss cross-operator opportunities, or on compiler pattern matching, which lacks generality. Standard DNN graphs overlook FHE-specific behaviors, while fully lowering to FHE-level operations creates excessive granularity and hinders optimization. We present FHEFusion, a compiler framework for the CKKS scheme that enables fusion through a new IR. This IR preserves high-level DNN semantics while introducing FHE-aware operators—masking and compaction (Strided_Slice)—that are central to CKKS, thereby exposing broader fusion opportunities. Guided by algebraic rules and an FHE-aware cost model, FHEFusion reduces multiplicative depth and identifies profitable fusions. This package contains artifact for the FHEFusion, include compiler source code, test models and scripts.