Overrides *generate* function to be able to use the model as a conditional generator

        Parameters:
            pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, image_height, image_width)`:
                Input image to be processed
            input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                The sequence used as a prompt for the generation.
            attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:


        Examples:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, TFBlipForConditionalGeneration

        >>> model = TFBlipForConditionalGeneration.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(images=image, return_tensors="tf")

        >>> outputs = model.generate(**inputs)
        >>> print(processor.decode(outputs[0], skip_special_tokens=True))
        two cats sleeping on a couch
        ```
        r