sNetForImageClassification
    >>> from PIL import Image
    >>> import jax
    >>> import requests

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
    >>> model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")

    >>> inputs = image_processor(images=image, return_tensors="np")
    >>> outputs = model(**inputs)
    >>> logits = outputs.logits

    >>> # model predicts one of the 1000 ImageNet classes
    >>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
    >>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
    ```
)8Ú	functoolsr