  are added after each decoder layer (with the FFNs sharing parameters).
If you want to train the model in a distributed environment across multiple nodes, then one should update the
  num_boxes variable in the DetrLoss class of modeling_detr.py. When training on multiple nodes, this should be
  set to the average number of target boxes across all nodes, as can be seen in the original implementation here.
[~transformers.DetrForObjectDetection] and [~transformers.DetrForSegmentation] can be initialized with
  any convolutional backbone available in the timm library.
  Initializing with a MobileNet backbone for example can be done by setting the backbone attribute of
  [~transformers.DetrConfig] to "tf_mobilenetv3_small_075", and then initializing the model with that
  config.
DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
