src/transformers/pipelines/zero_shot_image_classification.py/ZeroShotImageClassificationPipeline/__call__
class ZeroShotImageClassificationPipeline:    def __call__(self, images: Union[str, List[str], "Image", List["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

            candidate_labels (`List[str]`):
                The candidate labels for this image

            hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`):
                The sentence used in cunjunction with *candidate_labels* to attempt the image classification by
                replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
                logits_per_image

        Return:
            A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the
            following keys:

            - **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`.
            - **score** (`float`) -- The score attributed by the model for that label (between 0 and 1).
        """
        return super().__call__(images, **kwargs)