class_docstr: Implement RetinaNet in :paper:`RetinaNet`.
class_docstr_tok: ["Implement","RetinaNet","in",":","paper",":","`","RetinaNet","`","."]
code: def forward_training(
      self, images, features, predictions, gt_instances
  ):
      # Transpose the Hi*Wi*A dimension to the middle:
      (
          pred_logits,
          pred_anchor_deltas,
      ) = self._transpose_dense_predictions(
          predictions, [self.num_classes, 4]
      )
      anchors = self.anchor_generator(features)
      gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
      return self.losses(
          anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes
      )
  
imports: import logging
  import math
  from typing import List, Tuple
  import torch
  from fvcore.nn import sigmoid_focal_loss_jit
  from torch import Tensor, nn
  from torch.nn import functional
  from detectron2.config import configurable
  from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm
  from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
  from detectron2.utils.events import get_event_storage
  from ..anchor_generator import build_anchor_generator
  from ..backbone import Backbone, build_backbone
  from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
  from ..matcher import Matcher
  from .build import META_ARCH_REGISTRY
  from .dense_detector import DenseDetector, permute_to_N_HWA_K
label_desc: Use image node_id 3 for calling the RetinaNet obj's underlying member method code with example usage: obj.forward_training(images, features, predictions, gt_instances) and returns: self