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 forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
      if x.dim() == 4:
          torch._assert(
              list(x.shape[2:]) == [1, 1],
              f"x has the wrong shape, expecting the last two dimensions to be [1,1] instead of {list(x.shape[2:])}",
          )
      x = x.flatten(start_dim=1)
      scores = self.cls_score(x)
      bbox_deltas = self.bbox_pred(x)
  
      return scores, bbox_deltas
  
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 2 for calling the FastRCNNPredictor obj's underlying member method code with example usage: obj.forward(x) and returns: scores,  bbox_deltas