Okay got it! I ll come back with a working example/steps to you.
No, it wouldn’t when providing a custom inference.py the hub config should be ignored except you are not overwriting the model_fn.
You need to use model_fn and not load_fn.
Could you test instead of providing the inference.py in the model.tar.gz to provide it dynamically when creating the endpoint?
like that
    hf_model = HuggingFaceModel(
        model_data="s3://call-summarization/model1.tar.gz",  
        role=role,
        transformers_version="4.6.1", 
        pytorch_version="1.7.1",
        source_dir="code",
        py_version="py36",
        entry_point="inference.py",
    )

For this to work the file structure needs to be
code/
     inference.py
deploy.py