class_docstr: DeformableDETR model for object detection task.
docstr: Initialize DeformableDETR Detection model.
  
  Args:
    config: Configurations of the model.
    dataset_meta_data: Dataset meta data specifies `target_is_onehot`, which
      is False by default. The padded objects have label 0. The first
      legitimate object has label 1, and so on.
docstr_tok: ["Initialize","DeformableDETR","Detection","model",".","Args",":","config",":","Configurations","of","the","model",".","dataset_meta_data",":","Dataset","meta","data","specifies","`","target_is_onehot","`",",","which","is","False","by","default",".","The","padded","objects","have","label","0",".","The","first","legitimate","object","has","label","1",",","and","so","on","."]
code: def __init__(
      self,
      config: Optional[ml_collections.ConfigDict],
      dataset_meta_data: Dict[str, Any],
  ):
      """Initialize DeformableDETR Detection model.
  
      Args:
        config: Configurations of the model.
        dataset_meta_data: Dataset meta data specifies `target_is_onehot`, which
          is False by default. The padded objects have label 0. The first
          legitimate object has label 1, and so on.
      """
      if config is not None:
          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: import math
  from typing import Any, Dict, Optional, Sequence, Tuple, Union
  from absl import logging
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib import matchers
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import box_utils
  from scenic.model_lib.base_models import model_utils
  from scenic.projects.baselines.deformable_detr.backbone import DeformableDETRBackbone
  from scenic.projects.baselines.deformable_detr.backbone import InputPosEmbeddingSine
  from scenic.projects.baselines.deformable_detr.backbone import mask_for_shape
  from scenic.projects.baselines.deformable_detr.deformable_transformer import BBoxCoordPredictor
  from scenic.projects.baselines.deformable_detr.deformable_transformer import DeformableDETRTransformer
  from scenic.projects.baselines.deformable_detr.deformable_transformer import inverse_sigmoid
  from scenic.projects.baselines.deformable_detr.deformable_transformer import pytorch_kernel_init
label_desc: Use image node_id 1 to create a new DeformableDETRModel object from inherited base classes: base_model with example: obj = DeformableDETRModel(config, dataset_meta_data)