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

- **ID:** `tensorflow/custom-op-not-found`
- **Domain:** tensorflow
- **Category:** build_error
- **Error Code:** `E006`
- **Verification:** ai_generated
- **Fix Rate:** 80%

## Root Cause

A custom TensorFlow operation was not properly compiled or loaded, so the runtime cannot find its kernel.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow>=2.0.0 | active | — | — |
| tensorflow<=2.13.0 | active | — | — |

## Workarounds

1. **Compile the custom op using tf.load_op_library('path/to/custom_op.so') and ensure the .so file is in the same directory as the script.** (80% success)
   ```
   Compile the custom op using tf.load_op_library('path/to/custom_op.so') and ensure the .so file is in the same directory as the script.
   ```
2. **Use tf.raw_ops.CustomOp after ensuring the op is registered via a proper build script (e.g., using Bazel or CMake). Example: from tensorflow.python.framework import load_library; custom_op = load_library.load_op_library('custom_op.so')** (85% success)
   ```
   Use tf.raw_ops.CustomOp after ensuring the op is registered via a proper build script (e.g., using Bazel or CMake). Example: from tensorflow.python.framework import load_library; custom_op = load_library.load_op_library('custom_op.so')
   ```

## Dead Ends

- **Use pip install tensorflow-custom-op** — Assumes a pip package exists; usually custom ops are not distributed via pip. (90% fail)
- **Set PYTHONPATH to include the op source directory** — Does not compile the op; only adds source to path. (95% fail)