class FondantInferenceModel:
    """FondantInferenceModel class that abstracts the model loading and inference.
    User needs to implement an inference, pre/postprocess step and pass the class to the FondantInferenceComponent.
    The FondantInferenceComponent will then load the model and prepare it for inference.
    The examples folder can then show examples for a pytorch / huggingface / tensorflow / ... model.
    """
    def __init__(self, device: str = "cpu"):
        self.device = device
        # load model
        self.model = self.load_model()
        # set model to eval mode
        self.eval()
        
    def load_model(self):
        # load model
        ...
    
    def eval(self):
        # prepare for inference
        self.model = self.model.eval()
        self.model = self.model.to(self.device)
    
    def preprocess(self, input):
        # preprocess input
        ...
    
    def postprocess(self, output):
        # postprocess output
        ...
    
    def __call__(self, *args, **kwargs):
        processed_inputs = self.preprocess(*args, **kwargs)
        outputs = self.model(*processed_inputs)
        processed_outputs = self.postprocess(outputs)
        return processed_outputs
    

class FondantInferenceComponent(FondantTransformComponent, FondantInferenceModel):
    # loads the model and prepares it for inference
    
    def transform(
        self, args: argparse.Namespace, dataframe: dd.DataFrame
    ) -> dd.DataFrame:
        # by using the InferenceComponent, the model is automatically loaded and prepared for inference
        # you just need to call the infer method
        # the self.infer method calls the model.__call__ method of the FondantInferenceModel
        output = self.infer(args.image)