code: def __init__(
      self, opts, in_channels: int, dim_reduced: int, num_classes: int
  ) -> None:
      super().__init__(
          *[
              TransposeConvLayer2d(
                  opts,
                  in_channels=in_channels,
                  out_channels=dim_reduced,
                  kernel_size=2,
                  stride=2,
                  padding=0,
                  output_padding=0,
                  use_norm=False,
                  use_act=False,
                  bias=False,
                  groups=1,
              ),
              replace_syncbn_with_syncbnfp32(
                  opts, num_features=dim_reduced
              ),
              nn.ReLU(inplace=False),
              ConvLayer2d(
                  opts,
                  in_channels=dim_reduced,
                  out_channels=num_classes,
                  kernel_size=1,
                  stride=1,
                  bias=True,
                  use_norm=False,
                  use_act=False,
              ),
          ]
      )
  
      for layer in self.modules():
          if isinstance(layer, (nn.Conv2d, nn.ConvTranspose2d)):
              initialize_conv_layer(
                  module=layer, init_method="kaiming_normal"
              )
  
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 MaskRCNNPredictor object from inherited base classes: nn with example: obj = MaskRCNNPredictor(opts, in_channels, dim_reduced, num_classes)