class_docstr: A dummy set detection model for testing purposes.
class_docstr_tok: ["A","dummy","set","detection","model","for","testing","purposes","."]
code: def __init__(self, config=None, dataset_meta_data=None):
      dataset_meta_data = {
          "num_classes": NUM_CLASSES,
          "target_is_onehot": False,
      }
      self.losses_and_metrics = ["labels", "boxes"]
      config = config or ml_collections.ConfigDict()
      config.aux_loss = True
      config.bbox_loss_coef = BBOX_LOSS_COEF
      config.giou_loss_coef = GIOU_LOSS_COEF
      config.class_loss_coef = CE_LOSS_COEF
      config.eos_coef = EOS_COEF
      self.loss_terms_weights = {
          "loss_class": config.class_loss_coef,
          "loss_bbox": config.bbox_loss_coef,
          "loss_giou": config.giou_loss_coef,
      }
      super().__init__(config, dataset_meta_data)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from flax import jax_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.projects.baselines.detr import detr_base_model
label_desc: Use image node_id 1 to create a new MyObjectDetectionWithMatchingModel object from inherited base classes: detr_base_model with example: obj = MyObjectDetectionWithMatchingModel(config, dataset_meta_data)