class_docstr: A regression and classification head for use in RetinaNet.
  
  Args:
      in_channels (int): number of channels of the input feature
      num_anchors (int): number of anchors to be predicted
      num_classes (int): number of classes to be predicted
class_docstr_tok: ["A","regression","and","classification","head","for","use","in","RetinaNet",".","Args",":","in_channels","(","int",")",":","number","of","channels","of","the","input","feature","num_anchors","(","int",")",":","number","of","anchors","to","be","predicted","num_classes","(","int",")",":","number","of","classes","to","be","predicted"]
code: def __init__(self, in_channels, num_anchors, num_classes):
      super(RetinaNetHead, self).__init__()
      self.classification_head = RetinaNetClassificationHead(
          in_channels, num_anchors, num_classes
      )
      self.regression_head = RetinaNetRegressionHead(
          in_channels, num_anchors
      )
  
imports: import math
  import warnings
  from collections import OrderedDict
  from typing import Dict, List, Tuple, Optional, Union
  import torch
  from torch import nn, Tensor
  from .None import det_utils
  from .anchor_utils import AnchorsGenerator
  from .None import boxes
  from .losses import sigmoid_focal_loss
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 to create a new RetinaNetHead object from inherited base classes: nn with example: obj = RetinaNetHead(in_channels, num_anchors, num_classes)