code: def validate(model, criterion, data_loader, device, epoch):
      with torch.no_grad():
          model.eval()
          sums = defaultdict(lambda: 0.0)
          start = time()
  
          for waveform, specgram, target in data_loader:
              waveform = waveform.to(device)
              specgram = specgram.to(device)
              target = target.to(device)
  
              output = model(waveform, specgram)
              output, target = output.squeeze(1), target.squeeze(1)
  
              loss = criterion(output, target)
              sums["loss"] += loss.item()
  
          avg_loss = sums["loss"] / len(data_loader)
  
          metric = MetricLogger("validation")
          metric["epoch"] = epoch
          metric["loss"] = avg_loss
          metric["time"] = time() - start
          metric()
  
          return avg_loss
  
file_path: audio/examples/pipeline_wavernn/main.py
imports: import argparse
  import logging
  import os
  from collections import defaultdict
  from datetime import datetime
  from time import time
  from typing import List
  import torch
  import torchaudio
  from datasets import collate_factory, split_process_dataset
  from losses import LongCrossEntropyLoss, MoLLoss
  from processing import NormalizeDB
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torchaudio.models.wavernn import WaveRNN
  from utils import count_parameters, MetricLogger, save_checkpoint
label_desc: Use image node_id 3 for calling a global function with example usage: validate(model, criterion, data_loader, device, epoch) and returns: avg_loss