code: def model_fn(self, data):
      images, targets = data
      images = list(image.to(self.config.device) for image in images)
      images = torch.stack(images)
      images = images.float()
      targets = [
          {k: v.to(self.config.device) for k, v in t.items()}
          for t in targets
      ]
  
      boxes = [target["bboxes"].float() for target in targets]
      labels = [target["labels"].float() for target in targets]
      img_size = torch.stack(
          [target["img_size"] for target in targets]
      ).float()
      img_scale = torch.stack(
          [target["img_scale"] for target in targets]
      ).float()
  
      annotations = {
          "bbox": boxes,
          "cls": labels,
          "img_size": img_size,
          "img_scale": img_scale,
      }
  
      if self.config.fp16:
          with torch.cuda.amp.autocast():
              output, loss, metrics = self.model(images, annotations)
      else:
          output, loss, metrics = self.model(images, annotations)
      return output, loss, metrics
  
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 2 for calling the TezEfficientDet obj's underlying member method code with example usage: obj.model_fn(data) and returns: output,  loss,  metrics