code: def preprocess(self, image):
      image = tf.expand_dims(image, 0)
      image = image_preprocess(image, bgr=True)
      return tf.transpose(image, [0, 3, 1, 2])
  
imports: from tensorpack import tfv1
  from tensorpack import ModelDesc
  from tensorpack.models import GlobalAvgPooling, l2_regularizer, regularize_cost
  from tensorpack.tfutils import optimizer
  from tensorpack.tfutils.summary import add_moving_summary
  from config import config
  from data import get_all_anchors, get_all_anchors_fpn
  from utils.box_ops import area
  from .None import model_frcnn
  from .None import model_mrcnn
  from .backbone import image_preprocess, resnet_c4_backbone, resnet_conv5, resnet_fpn_backbone
  from .model_box import RPNAnchors, clip_boxes, crop_and_resize, roi_align
  from .model_cascade import CascadeRCNNHead
  from .model_fpn import fpn_model, generate_fpn_proposals, multilevel_roi_align, multilevel_rpn_losses
  from .model_frcnn import BoxProposals, FastRCNNHead, fastrcnn_outputs, fastrcnn_predictions, sample_fast_rcnn_targets
  from .model_mrcnn import maskrcnn_loss, maskrcnn_upXconv_head, unpackbits_masks
  from .model_rpn import generate_rpn_proposals, rpn_head, rpn_losses
label_desc: Use image node_id 1 for calling the GeneralizedRCNN obj's underlying member method code with example usage: obj.preprocess(image) and returns: tf