rel_logits vs pred_logits about reltr HOT 2 CLOSED

"""
 The forward expects a NestedTensor, which consists of:
               - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
               - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
            It returns a dict with the following elements:
               - "pred_logits": the entity classification logits (including no-object) for all entity queries.
                                Shape= [batch_size x num_queries x (num_classes + 1)]
               - "pred_boxes": the normalized entity boxes coordinates for all entity queries, represented as
                               (center_x, center_y, height, width). These values are normalized in [0, 1],
                               relative to the size of each individual image (disregarding possible padding).
                               See PostProcess for information on how to retrieve the unnormalized bounding box.
               - "sub_logits": the subject classification logits
               - "obj_logits": the object classification logits
               - "sub_boxes": the normalized subject boxes coordinates
               - "obj_boxes": the normalized object boxes coordinates
               - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
                                dictionnaries containing the two above keys for each decoder layer.
"""