ss is multiplied by if during pretraining, one
            decided to train with multiple vision-based loss objectives.
        num_qa_labels (`int`, *optional*, defaults to 9500):
            This represents the total number of different question answering (QA) labels there are. If using more than
            one dataset with QA, the user will need to account for the total number of labels that all of the datasets
            have in total.
        num_object_labels (`int`, *optional*, defaults to 1600):
            This represents the total number of semantically unique objects that lxmert will be able to classify a
            pooled-object feature as belonging too.
        num_attr_labels (`int`, *optional*, defaults to 400):
            This represents the total number of semantically unique attributes that lxmert will be able to classify a
            pooled-object feature as possessing.
        task_matched (`bool`, *optional*, defaults to `True`):
            This task is used for sentence-image matching. If the sentence correctly describes the image the label will
            be 1. If the sentence does not correctly describe the image, the label will be 0.
        task_mask_lm (`bool`, *optional*, defaults to `True`):
            Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
            objective.
        task_obj_predict (`bool`, *optional*, defaults to `True`):
            Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
        task_qa (`bool`, *optional*, defaults to `True`):
            Whether or not to add the question-answering loss to the objective
        visual_obj_loss (`bool`, *optional*, defaults to `True`):
            Whether or not to calculate the object-prediction loss objective
        visual_attr_loss (`bool`, *optional*, defaults to `True`):
            Whether or not to calculate the attribute-prediction loss objective
        visual_feat_loss (`bool`, *optional*, defaults to `True`):
            Whether or not to calculate the feature-regression loss objective
        output_attentions (`bool`, *optional*, defaults to `False`):
            Whether or not the model should return the attentions from the vision, language, and cross-modality layers
            should be returned.
        output_hidden_states (`bool`, *optional*, defaults to `False`):
            Whether or not the model should return the hidden states from the vision, language, and cross-modality
            layers should be returned.
    Z