code: def inference(
      self, model, inputs, weighted_batch=False, group_ratio=None
  ):
      output = model(*inputs)
      if weighted_batch:
          y = torch.ones(output.shape).to(output.get_device())
          for i in range(self.batch_size):
              for j in range(len(group_ratio)):
                  y[i * len(group_ratio) + j] *= group_ratio[j]
          output = (output * y).mean() * self.num_strata
      else:
          output = output.mean()
      if isinstance(output, (list, tuple)):
          loss = output[0]
      else:
          loss = output
      return loss
  
file_path: gluonts/src/gluonts/nursery/SCott/trainers/SAdagrad.py
imports: import time
  from typing import List, Optional
  import torch
  import torch.nn
  from torch.utils.tensorboard import SummaryWriter
  import copy
  import random
  import math
  from pts.trainers.timer import Timer
label_desc: Use image node_id 2 for calling the SAdagrad obj's underlying member method code with example usage: obj.inference(model, inputs, weighted_batch, group_ratio) and returns: loss