code: def create_model(
      num_classes=1, image_size=512, architecture="tf_efficientnetv2_l"
  ):
      efficientdet_model_param_dict["tf_efficientnetv2_l"] = dict(
          name="tf_efficientnetv2_l",
          backbone_name="tf_efficientnetv2_l",
          backbone_args=dict(drop_path_rate=0.2),
          num_classes=num_classes,
          url="",
      )
  
      config = get_efficientdet_config(architecture)
      config.update({"num_classes": num_classes})
      config.update({"image_size": (image_size, image_size)})
  
      net = EfficientDet(config, pretrained_backbone=True)
      net.class_net = HeadNet(
          config,
          num_outputs=config.num_classes,
      )
      return DetBenchTrain(net, config)
  
imports: import argparse
  import ast
  import os
  import albumentations
  import numpy
  import pandas
  import timm
  import torch
  import torch.nn
  import torchvision
  from effdet import DetBenchTrain, EfficientDet, get_efficientdet_config
  from effdet.config.model_config import efficientdet_model_param_dict
  from effdet.efficientdet import HeadNet
  from PIL import Image, ImageFile
  from sklearn.model_selection import train_test_split
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
  from tez import Tez, TezConfig
label_desc: Use image node_id 4 for calling a global function with example usage: create_model(num_classes, image_size, architecture) and returns: DetBenchTrain