class_docstr: Base model for object detection with matching.
class_docstr_tok: ["Base","model","for","object","detection","with","matching","."]
docstr: Returns a dict that lists all losses for this model.
docstr_tok: ["Returns","a","dict","that","lists","all","losses","for","this","model","."]
code: def loss_and_metrics_map(
      self,
  ) -> Dict[str, Callable[..., Tuple[ArrayDict, MetricsDict]]]:
      """Returns a dict that lists all losses for this model."""
      return {
          **super().loss_and_metrics_map,
          "boxes": self.boxes_losses_and_metrics,
      }
  
imports: import abc
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple
  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.owl_vit import losses
label_desc: Use image node_id 2 for calling the ObjectDetectionModel obj's underlying member method code with example usage: obj.loss_and_metrics_map() and returns: dict