code: def train_step(self, model, optimizer, data, loss_ids):
      optimizer.zero_grad()
  
      output = model(data)
  
      # Call backward for num_losses-1
      for idx in loss_ids:
          loss = output.mean()
          with amp.scale_loss(
              loss, optimizer, loss_id=idx
          ) as scaled_loss:
              scaled_loss.backward(retain_graph=True)
  
      optimizer.step()
      return output
  
imports: import unittest
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from apex import amp
  from utils import common_init, FLOAT
label_desc: Use image node_id 4 for calling the TestCheckpointing obj's underlying member method code with example usage: obj.train_step(model, optimizer, data, loss_ids) and returns: output