code: def model_fn(self, data):
      for key, value in data.items():
          data[key] = value.to(self.device)
      if self.fp16:
          with torch.cuda.amp.autocast():
              output, loss, metrics = self(**data)
      else:
          output, loss, metrics = self(**data)
      return output, loss, metrics
  
imports: import warnings
  import psutil
  import torch
  import torch.nn
  from tqdm import tqdm
  from tez import enums
  from tez.callbacks import CallbackRunner
  from tez.utils import AverageMeter
label_desc: Use image node_id 13 for calling the Model obj's underlying member method code with example usage: obj.model_fn(data) and returns: output,  loss,  metrics