ONNX Model Inference Operator

### Description ONNX (Open Neural Network Exchange) provides cross-platform compatibility An operator that can run inference using ONNX models, ideal for deploying machine learning models in a standardized format can provide us with direct model invocation. this can be solved using a pythonOperator ofc as onnxruntime can be executed with pythonruntime, but this can also be built into airflow to minimize work, a simple onnx operator structure would be something like: ``` import onnxruntime as ort from airflow import DAG from airflow.operators.python import PythonOperator from datetime import datetime def run_onnx_inference(): # Load the ONNX model model_path = '/path/to/your/model.onnx' session = ort.InferenceSession(model_path) # Prepare input data input_name = session.get_inputs()[0].name input_data = {"your_input_key": your_input_data} # Run inference result = session.run(None, {input_name: input_data}) print(result) # Define the DAG with DAG( dag_id='onnx_inference_dag', start_date=datetime(2023, 1, 1), schedule_interval='@once' ) as dag: # Define the task inference_task = PythonOperator( task_id='onnx_inference_task', python_callable=run_onnx_inference ) ``` Looking frwd to any suggestions. ### Use case/motivation A direct support of onnx with Airflow's DAG-based orchestration can manage the entire lifecycle of data processing and model inference in one place, providing a more cohesive and manageable workflow. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)