class_docstr: DeformableDETR model for object detection task.
docstr: Loss and metrics function for DeformableDETR.
  
  Args:
    outputs: Model prediction. The exact fields depend on the losses used.
      Please see labels_losses_and_metrics and boxes_losses_and_metrics for
      details.
    batch: Dict that has 'inputs', 'batch_mask' and, 'label' (ground truth).
      batch['label'] is a dict where the keys and values depend on the losses
      used. Please see labels_losses_and_metrics and boxes_losses_and_metrics
      member methods.
    matches: Possibly pass in matches if already done.
    model_params: Pass in model params if we are doing L2 regularization.
  
  Returns:
    total_loss: Total loss weighted appropriately.
    metrics_dict: Individual loss terms for logging purposes.
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code: def loss_and_metrics_function(
      self,
      outputs: ArrayDict,
      batch: ArrayDict,
      matches: Optional[Sequence[jnp.ndarray]] = None,
      model_params: Optional[jnp.ndarray] = None,
  ) -> Tuple[jnp.ndarray, MetricsDict]:
      """Loss and metrics function for DeformableDETR.
  
      Args:
        outputs: Model prediction. The exact fields depend on the losses used.
          Please see labels_losses_and_metrics and boxes_losses_and_metrics for
          details.
        batch: Dict that has 'inputs', 'batch_mask' and, 'label' (ground truth).
          batch['label'] is a dict where the keys and values depend on the losses
          used. Please see labels_losses_and_metrics and boxes_losses_and_metrics
          member methods.
        matches: Possibly pass in matches if already done.
        model_params: Pass in model params if we are doing L2 regularization.
  
      Returns:
        total_loss: Total loss weighted appropriately.
        metrics_dict: Individual loss terms for logging purposes.
      """
      tgt_labels, tgt_boxes = _targets_from_batch(
          batch, self.config.get("target_is_onehot", False)
      )
      if matches is None:
          indices, aux_indices = None, None
      else:
          indices, *aux_indices = matches
      losses = self.compute_loss_for_layer(
          pred_logits=outputs["pred_logits"],
          tgt_labels=tgt_labels,
          pred_boxes=outputs["pred_boxes"],
          tgt_boxes=tgt_boxes,
          indices=indices,
      )
  
      if "aux_outputs" in outputs:
          for i, aux_outputs in enumerate(outputs["aux_outputs"]):
              aux_losses = self.compute_loss_for_layer(
                  pred_logits=aux_outputs["pred_logits"],
                  tgt_labels=tgt_labels,
                  pred_boxes=aux_outputs["pred_boxes"],
                  tgt_boxes=tgt_boxes,
                  indices=aux_indices[i]
                  if aux_indices is not None
                  else None,
              )
              aux_losses = {
                  f"{k}_aux{i}": v for k, v in aux_losses.items()
              }
              losses.update(aux_losses)
  
      ntargets = jnp.sum(tgt_labels > 0, axis=1)
      norm_type = self.config.get("normalization", "detr")
      logging.info("Normalization type: %s", norm_type)
      if norm_type == "detr":
          ntargets = jnp.maximum(ntargets.sum(), 1.0)
      elif norm_type == "global":
          ntargets = jax.lax.pmean(ntargets.sum(), axis_name="batch")
          ntargets = jnp.maximum(ntargets, 1.0)
      else:
          raise ValueError(f"Unknown normalization {norm_type}.")
  
      # Normalize losses by num_boxes.
      losses = jax.tree_util.tree_map(lambda x: x / ntargets, losses)
  
      if self.config.get("l2_decay_factor", 0) > 0:
          l2_loss = model_utils.l2_regularization(model_params)
          losses["l2_loss"] = (
              0.5 * self.config.l2_decay_factor * l2_loss
          )
  
      # Sum total loss.
      losses["total_loss"] = jax.tree_util.tree_reduce(
          jnp.add, losses, 0
      )  # pytype: disable=wrong-arg-types  # numpy-scalars
  
      # Store metrics for logging.
      metrics = {k: (v, 1.0) for k, v in losses.items()}
      for k, v in metrics.items():
          metrics[k] = model_utils.psum_metric_normalizer(
              v
          )  # pytype: disable=wrong-arg-types  # jax-ndarray
  
      return losses["total_loss"], metrics
  
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 5 for calling the DeformableDETRModel obj's underlying member method code with example usage: obj.loss_and_metrics_function(outputs, batch, matches, model_params) and returns: losses,  metrics