class_docstr: Standard classification + bounding box regression layers
  for Fast R-CNN.
  
  Args:
      in_channels (int): number of input channels
      num_classes (int): number of output classes (including background)
class_docstr_tok: ["Standard","classification","+","bounding","box","regression","layers","for","Fast","R-CNN",".","Args",":","in_channels","(","int",")",":","number","of","input","channels","num_classes","(","int",")",":","number","of","output","classes","(","including","background",")"]
code: def __init__(self, in_channels, num_classes):
      super().__init__()
      self.cls_score = nn.Linear(in_channels, num_classes)
      self.bbox_pred = nn.Linear(in_channels, num_classes * 4)
  
imports: from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .anchor_utils import AnchorGenerator
  from .backbone_utils import _mobilenet_extractor, _resnet_fpn_extractor, _validate_trainable_layers
  from .generalized_rcnn import GeneralizedRCNN
  from .roi_heads import RoIHeads
  from .rpn import RegionProposalNetwork, RPNHead
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 to create a new FastRCNNPredictor object from inherited base classes: nn with example: obj = FastRCNNPredictor(in_channels, num_classes)