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)
code: def __init__(self, in_channels: int, num_classes: int) -> None:
      super().__init__()
      self.cls_score = LinearLayer(in_channels, num_classes, bias=True)
      self.bbox_pred = LinearLayer(
          in_channels, num_classes * 4, bias=True
      )
  
      for layer in self.modules():
          if isinstance(layer, LinearLayer):
              initialize_fc_layer(
                  module=layer, init_method="kaiming_uniform"
              )
  
imports: from typing import List, Tuple
  import torch
  from torch import Tensor, nn
  from cvnets.layers import ConvLayer2d, LinearLayer, TransposeConvLayer2d, get_normalization_layer
  from cvnets.misc.init_utils import initialize_conv_layer, initialize_fc_layer
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)