class_docstr: Base model for object detection with matching.
docstr: Initialize Detection model.
  
  Args:
    config: Hyper-parameter dictionary.
    dataset_meta_data: Dataset meta data specifies `target_is_onehot`, which
      is False by default, and a required `num_classes`, which is the number
      of object classes including background/unlabeled/padding. The padded
      objects have label 0. The first legitimate object has label 1, and so
      on.
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code: def __init__(
      self,
      config: Optional[ml_collections.ConfigDict],
      dataset_meta_data: Dict[str, Any],
  ):
      """Initialize Detection model.
  
      Args:
        config: Hyper-parameter dictionary.
        dataset_meta_data: Dataset meta data specifies `target_is_onehot`, which
          is False by default, and a required `num_classes`, which is the number
          of object classes including background/unlabeled/padding. The padded
          objects have label 0. The first legitimate object has label 1, and so
          on.
      """
      super().__init__(config, dataset_meta_data)
      self.losses_and_metrics.append("boxes")
      if config is not None:
          self.loss_terms_weights["loss_bbox"] = config.bbox_loss_coef
          self.loss_terms_weights["loss_giou"] = config.giou_loss_coef
  
imports: import abc
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  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
label_desc: Use image node_id 1 to create a new ObjectDetectionWithMatchingModel object from inherited base classes: BaseModelWithMatching with example: obj = ObjectDetectionWithMatchingModel(config, dataset_meta_data)