src/transformers/pipelines/image_classification.py/ImageClassificationPipeline/__call__
class ImageClassificationPipeline:    def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
        """
        Assign labels to the image(s) passed as inputs.

        Args:
            images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
                The pipeline handles three types of images:

                - A string containing a http link pointing to an image
                - A string containing a local path to an image
                - An image loaded in PIL directly

                The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
                Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
                images.
            top_k (`int`, *optional*, defaults to 5):
                The number of top labels that will be returned by the pipeline. If the provided number is higher than
                the number of labels available in the model configuration, it will default to the number of labels.

        Return:
            A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
            dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
            the images.

            The dictionaries contain the following keys:

            - **label** (`str`) -- The label identified by the model.
            - **score** (`int`) -- The score attributed by the model for that label.
        """
        return super().__call__(images, **kwargs)