code: def get_all_outputs_and_labels(inputs, labels, model, iteration):
      all_inputs = torch.cat(inputs[iteration], dim=0).to(TEST_DEVICE)
      all_labels = torch.cat(labels[iteration], dim=0).to(TEST_DEVICE)
      all_outputs = model(all_inputs)
      return all_outputs, all_labels
  
imports: import logging
  import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory
  from pytorch_metric_learning.miners import PairMarginMiner
  from pytorch_metric_learning.utils import distributed
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 9 for calling a global function with example usage: get_all_outputs_and_labels(inputs, labels, model, iteration) and returns: all_outputs,  all_labels