if (!status->ok()) {
    return nullptr;
  }
  // We can't use std::make_unique here because of its interaction with a
  // private constructor: https://abseil.io/tips/134
  return std::unique_ptr<Runtime>(new Runtime(result));
}

}  // namespace cc
}  // namespace experimental
}  // namespace tensorflow

arguments with types `tensor<?x?xi32>, tensor<?x?xi32>, tensor<?x?xi32>`.

```bash
tf-to-stablehlo-translate <saved-model-path> --input-arg-shapes=1,12:1,12:1,12
```

### Dependencies

* TensorFlow
* MLIR
* Abseil (absl)

## Python APIs


### `savedmodel_to_stablehlo`

Converts a TensorFlow SavedModel into StableHLO bytecode.

```Python

  }

  if (!status->ok()) {
    return nullptr;
  }

  // We can't use std::make_unique here because of its interaction with a
  // private constructor: https://abseil.io/tips/134
  return std::unique_ptr<SavedModelAPI>(new SavedModelAPI(saved_model));
}

inline ConcreteFunction* SavedModelAPI::GetConcreteFunction(
    const std::string& function_path, Status* status) {

    *   Added a 'experimental_payloads' field to tf.errors.OpError and its
        subclasses to support more detailed error reporting. This is inspired
        from Abseil Status payloads:
        https://github.com/abseil/abseil-cpp/blob/master/absl/status/status.h

*   `tf.summary`:

    *   New `tf.summary.graph` allows manual write of TensorFlow graph