Loading checkpoints while training a Faster-RCNN model on a custom dataset

I'm trying to load checkpoints and populate model weights using The Faster-RCNN architecture (Faster R-CNN ResNet50 V1 640x640 to be precise, from here. I'm trying to load the weights for this network similar to how it's done in the example notebook for RetinaNet, where they do the following:

fake_box_predictor = tf.compat.v2.train.Checkpoint(
    _base_tower_layers_for_heads=detection_model._box_predictor._base_tower_layers_for_heads,
    _box_prediction_head=detection_model._box_predictor._box_prediction_head,
)

fake_model = tf.compat.v2.train.Checkpoint(
          _feature_extractor=detection_model._feature_extractor,
          _box_predictor=fake_box_predictor
)

ckpt = tf.compat.v2.train.Checkpoint(model=fake_model)
ckpt.restore(checkpoint_path).expect_partial()

I'm trying to get a similar checkpoint loading mechanism going for the Faster-RCNN network I want to use, but the properties like _base_tower_layers_for_heads, _box_prediction_head only exist for the architecture used in the example, and not for anything else.

I also couldn't find documentation on which parts of the model to populate using Checkpoint for my particular use case. Would greatly appreciate any help on how to approach this!

from Loading checkpoints while training a Faster-RCNN model on a custom dataset