```
Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
without requiring users to understand new kind of objects. It's also relatively common to support many different types
of arguments for ease of use (audio files, can be filenames, URLs or pure bytes)
Adding it to the list of supported tasks
Go to src/transformers/pipelines/__init__.py and fill in SUPPORTED_TASKS with your newly created pipeline.
If possible it should provide a default model.
Adding tests
Create a new file tests/test_pipelines_MY_PIPELINE.py with example with the other tests.
The run_pipeline_test function will be very generic and run on small random models on every possible
architecture as defined by model_mapping and tf_model_mapping.
This is very important to test future compatibility, meaning if someone adds a new model for
