code: def forward(self, image, targets=None):
      x = self.model(image)
      if targets is not None:
          loss = nn.CrossEntropyLoss()(x, targets)
          metrics = self.monitor_metrics(x, targets)
          return x, loss, metrics
      return x, 0, {}
  
imports: import argparse
  import os
  import albumentations
  import numpy
  import pandas
  import timm
  import torch
  import torch.nn
  from cv2 import dft
  from sklearn import metrics, model_selection
  from tqdm.auto import tqdm
  from tez import Tez, TezConfig
  from tez.callbacks import EarlyStopping
  from tez.datasets import ImageDataset
  from tez.utils import seed_everything
label_desc: Use image node_id 4 for calling the SorghumModel obj's underlying member method code with example usage: obj.forward(image, targets) and returns: x,  int,  dict, x,  loss,  metrics