code: def forward(self, images, targets):
      outputs = self.base_model(images, targets)
      if targets is not None:
          loss = outputs["loss"]
          return outputs, loss, {}
      return outputs, 0, {}
  
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 3 for calling the WheatModel obj's underlying member method code with example usage: obj.forward(images, targets) and returns: outputs,  int,  dict, outputs,  loss,  dict