torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
            Contrastive loss for image-text similarity.
        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
            similarity scores.
        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
            similarity scores.
        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
            The text embeddings obtained by applying the projection layer to the pooled output of [`AlignTextModel`].
        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
            The output of [`AlignVisionModel`].
        text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`):
            The output of the [`AlignTextModel`].
        vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`):
            The output of the [`AlignVisionModel`].
    N