code: def evaluate(
      model,
      criterion,
      data_loader,
      decoder,
      language_model,
      device,
      epoch,
      disable_logger=False,
  ):
      with torch.no_grad():
          model.eval()
          start = time()
          metric = MetricLogger("validation", disable=disable_logger)
          metric["epoch"] = epoch
  
          for (
              inputs,
              targets,
              tensors_lengths,
              target_lengths,
          ) in data_loader:
              inputs = inputs.to(device, non_blocking=True)
              targets = targets.to(device, non_blocking=True)
  
              # keep batch first for data parallel
              outputs = model(inputs).transpose(-1, -2).transpose(0, 1)
  
              # CTC
              # outputs: input length, batch size, number of classes (including blank)
              # targets: batch size, max target length
              # input_lengths: batch size
              # target_lengths: batch size
  
              metric["cumulative loss"] += criterion(
                  outputs, targets, tensors_lengths, target_lengths
              ).item()
  
              metric["dataset length"] += len(inputs)
              metric["iteration"] += 1
  
              compute_error_rates(
                  outputs, targets, decoder, language_model, metric
              )
  
          metric["average loss"] = (
              metric["cumulative loss"] / metric["iteration"]
          )
          metric["validation time"] = time() - start
          metric()
  
          return metric["average loss"]
  
imports: import argparse
  import logging
  import os
  import string
  from datetime import datetime
  from time import time
  import torch
  import torchaudio
  from ctc_decoders import GreedyDecoder
  from datasets import collate_factory, split_process_librispeech
  from languagemodels import LanguageModel
  from torch.optim import Adadelta, Adam, AdamW, SGD
  from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from torchaudio.functional import edit_distance
  from torchaudio.models.wav2letter import Wav2Letter
  from transforms import Normalize, UnsqueezeFirst
  from utils import count_parameters, MetricLogger, save_checkpoint
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(model, criterion, data_loader, decoder, language_model, device, epoch, disable_logger) and returns: metric