def call(
        self,
        input_ids: tf.Tensor | None = None,
        pixel_values: tf.Tensor | None = None,
        attention_mask: tf.Tensor | None = None,
        position_ids: tf.Tensor | None = None,
        return_loss: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = None,
    ) -> Union[Tuple, TFBlipOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, TFBlipModel

        >>> model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
        >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

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

        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
        ... )

        >>> outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = tf.nn.softmax(logits_per_image, axis=1)  # we can take the softmax to get the label probabilities
        ```"""
        outputs = self.blip(
            input_ids=input_ids,
            pixel_values=pixel_values,
            attention_mask=attention_mask,
            position_ids=position_ids,
            return_loss=return_loss,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        return outputs

        Returns:

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, TFBlipModel

        >>> model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
        >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

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

        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
        ... )

        >>> outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = tf.nn.softmax(logits_per_image, axis=1)  # we can take the softmax to get the label probabilities
        ```
dpkd/transformers/src/transformers/models/blip/modeling_tf_blip.py