class_docstr: Standard classification + bounding box regression layers
  for Fast R-CNN.
  
  Arguments:
      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",".","Arguments",":","in_channels","(","int",")",":","number","of","input","channels","num_classes","(","int",")",":","number","of","output","classes","(","including","background",")"]
code: def forward(self, x):
      if x.dim() == 4:
          assert list(x.shape[2:]) == [1, 1]
      x = x.flatten(start_dim=1)
      scores = self.cls_score(x)
      bbox_deltas = self.bbox_pred(x)
  
      return scores, bbox_deltas
  
imports: import warnings
  from collections import OrderedDict
  from typing import Tuple, List, Dict, Optional, Union
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  from torchvision.ops import MultiScaleRoIAlign
  from .roi_head import RoIHeads
  from .transform import GeneralizedRCNNTransform
  from .rpn_function import AnchorsGenerator, RPNHead, RegionProposalNetwork
label_desc: Use image node_id 2 for calling the FastRCNNPredictor obj's underlying member method code with example usage: obj.forward(x) and returns: scores,  bbox_deltas