class_docstr: DeformableDETR model for object detection task.
docstr: Loss and metrics function for single prediction layer.
docstr_tok: ["Loss","and","metrics","function","for","single","prediction","layer","."]
code: def compute_loss_for_layer(
      self,
      tgt_labels: jnp.ndarray,
      pred_logits: jnp.ndarray,
      tgt_boxes: jnp.ndarray,
      pred_boxes: jnp.ndarray,
      indices: Optional[jnp.ndarray] = None,
  ) -> ArrayDict:
      """Loss and metrics function for single prediction layer."""
      target_is_onehot = self.dataset_meta_data.get(
          "target_is_onehot", False
      )
      if indices is None:
          pred_prob = self.logits_to_probs(pred_logits)
          cost, n_cols = compute_cost(
              tgt_labels=tgt_labels,
              out_prob=pred_prob,
              tgt_bbox=tgt_boxes,
              out_bbox=pred_boxes,
              alpha=self.config.focal_loss_alpha,
              gamma=self.config.focal_loss_gamma,
              class_loss_coef=self.config.class_loss_coef,
              bbox_loss_coef=self.config.bbox_loss_coef,
              giou_loss_coef=self.config.giou_loss_coef,
              target_is_onehot=target_is_onehot,
          )
          indices = matchers.hungarian_matcher(cost, n_cols=n_cols)
  
      losses = {}
      # Class loss.
      losses.update(
          loss_labels(
              pred_logits=pred_logits,
              tgt_labels=tgt_labels,
              indices=indices,
              alpha=self.config.focal_loss_alpha,
              gamma=self.config.focal_loss_gamma,
              class_loss_coef=self.config.class_loss_coef,
              target_is_onehot=target_is_onehot,
          )
      )
      # Boxes loss.
      losses.update(
          loss_boxes(
              src_boxes=pred_boxes,
              tgt_labels=tgt_labels,
              tgt_boxes=tgt_boxes,
              indices=indices,
              bbox_loss_coef=self.config.bbox_loss_coef,
              giou_loss_coef=self.config.giou_loss_coef,
              target_is_onehot=target_is_onehot,
          )
      )
      return losses
  
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 3 for calling the DeformableDETRModel obj's underlying member method code with example usage: obj.compute_loss_for_layer(tgt_labels, pred_logits, tgt_boxes, pred_boxes, indices) and returns: losses