code: def __init__(self, in_channels, dim_reduced, num_classes):
      super().__init__(
          OrderedDict(
              [
                  (
                      "conv5_mask",
                      nn.ConvTranspose2d(
                          in_channels, dim_reduced, 2, 2, 0
                      ),
                  ),
                  ("relu", nn.ReLU(inplace=True)),
                  (
                      "mask_fcn_logits",
                      nn.Conv2d(dim_reduced, num_classes, 1, 1, 0),
                  ),
              ]
          )
      )
      # initial params
      for name, param in self.named_parameters():
          if "weight" in name:
              nn.init.kaiming_normal_(
                  param, mode="fan_out", nonlinearity="relu"
              )
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/network_files/mask_rcnn.py
imports: from collections import OrderedDict
  import torch.nn
  from torchvision.ops import MultiScaleRoIAlign
  from .faster_rcnn_framework import FasterRCNN
label_desc: Use image node_id 1 to create a new MaskRCNNPredictor object from inherited base classes: nn with example: obj = MaskRCNNPredictor(in_channels, dim_reduced, num_classes)