# tensorflow.python.framework.errors_impl.InternalError: OpKernel registration failed: Could not find 'CustomOp' in the list of registered ops

- **ID:** `tensorflow/opkernel-registration-failed`
- **Domain:** tensorflow
- **Category:** build_error
- **Error Code:** `CUST`
- **Verification:** ai_generated
- **Fix Rate:** 88%

## Root Cause

A custom TensorFlow operation was compiled for a different TF version or ABI, causing registration to fail at runtime.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow 2.15 | active | — | — |
| tensorflow 2.16 | active | — | — |
| tensorflow 2.17 | active | — | — |

## Workarounds

1. **Rebuild the custom op library using `bazel build //tensorflow/core/user_ops:custom_op.so` with the same TensorFlow source version as the runtime. Then load it via `tf.load_op_library('./custom_op.so')`.** (88% success)
   ```
   Rebuild the custom op library using `bazel build //tensorflow/core/user_ops:custom_op.so` with the same TensorFlow source version as the runtime. Then load it via `tf.load_op_library('./custom_op.so')`.
   ```
2. **Use `tf.raw_ops` as a fallback if the custom op is a standard operation with a different name.** (60% success)
   ```
   Use `tf.raw_ops` as a fallback if the custom op is a standard operation with a different name.
   ```

## Dead Ends

- **** — Reinstalling TensorFlow via pip does not resolve custom op registration failures because the custom op library must be rebuilt against the exact installed version. (90% fail)
- **** — Copying the .so file from another machine often fails due to glibc or CUDA version mismatches. (85% fail)