code: def validation(
      model,
      data_loader,
      inference_model,
      sequence_sampler,
      tgt_tokenizer,
      ctx_l,
  ):
      """Validate the model on the dataset
  
      Parameters
      ----------
      model : TransformerModel
          The transformer model
      data_loader : DataLoader
          DataLoader
      inference_model
          The model for inference
      sequence_sampler:
          The sequence sampler for doing beam search
      tgt_tokenizer
          The target tokenizer
      ctx_l : list
          List of mx.ctx.Context
  
      Returns
      -------
      avg_nll_loss : float
          The average negative log-likelihood loss
      ntokens : int
          The total number of tokens
      pred_sentences
          The predicted sentences. Each element will be a numpy array.
      pred_lengths
          The length of the predicted sentences.
      sentence_ids
          IDs of the predicted sentences.
      """
      avg_nll_loss = mx.np.array(0, dtype=np.float32, ctx=mx.cpu())
      ntokens = 0
      pred_sentences = []
      sentence_ids = []
      pred_lengths = []
      for sample_data_l in grouper(data_loader, len(ctx_l)):
          loss_l = []
          ntokens += sum(
              [
                  ele[3].sum().asnumpy() - ele[0].shape[0]
                  for ele in sample_data_l
                  if ele is not None
              ]
          )
          for sample_data, ctx in zip(sample_data_l, ctx_l):
              if sample_data is None:
                  continue
              (
                  src_token_ids,
                  tgt_token_ids,
                  src_valid_length,
                  tgt_valid_length,
                  sample_ids,
              ) = sample_data
              src_token_ids = src_token_ids.as_in_ctx(ctx)
              tgt_token_ids = tgt_token_ids.as_in_ctx(ctx)
              src_valid_length = src_valid_length.as_in_ctx(ctx)
              tgt_valid_length = tgt_valid_length.as_in_ctx(ctx)
              if model.layout == "NT":
                  tgt_pred = model(
                      src_token_ids,
                      src_valid_length,
                      tgt_token_ids[:, :-1],
                      tgt_valid_length - 1,
                  )
                  tgt_labels = tgt_token_ids[:, 1:]
                  tgt_pred_logits = mx.npx.log_softmax(
                      tgt_pred, axis=-1
                  )
                  nll_loss = -mx.npx.pick(
                      tgt_pred_logits, tgt_labels, axis=-1
                  )
                  loss = mx.npx.sequence_mask(
                      nll_loss,
                      sequence_length=tgt_valid_length - 1,
                      use_sequence_length=True,
                      axis=1,
                  )
                  loss_l.append(loss.sum())
              elif model.layout == "TN":
                  tgt_pred = model(
                      src_token_ids.T,
                      src_valid_length,
                      tgt_token_ids.T[:-1, :],
                      tgt_valid_length - 1,
                  )
                  tgt_labels = tgt_token_ids.T[1:, :]
                  tgt_pred_logits = mx.npx.log_softmax(
                      tgt_pred, axis=-1
                  )
                  nll_loss = -mx.npx.pick(
                      tgt_pred_logits, tgt_labels, axis=-1
                  )
                  loss = mx.npx.sequence_mask(
                      nll_loss,
                      sequence_length=tgt_valid_length - 1,
                      use_sequence_length=True,
                      axis=0,
                  )
                  loss_l.append(loss.sum())
              init_input = mx.np.array(
                  [
                      tgt_tokenizer.vocab.bos_id
                      for _ in range(src_token_ids.shape[0])
                  ],
                  ctx=ctx,
              )
  
              # Perform beam search
              if model.layout == "NT":
                  states = inference_model.init_states(
                      src_token_ids, src_valid_length
                  )
              elif model.layout == "TN":
                  states = inference_model.init_states(
                      src_token_ids.T, src_valid_length
                  )
              samples, scores, sample_valid_length = sequence_sampler(
                  init_input, states, src_valid_length
              )
              samples = samples.asnumpy()
              sample_valid_length = sample_valid_length.asnumpy()
              for j in range(samples.shape[0]):
                  valid_length = sample_valid_length[j, 0]
                  # Ignore the BOS + EOS tokens
                  pred_sentences.append(
                      samples[j, 0, 1 : (valid_length - 1)]
                  )
                  pred_lengths.append(valid_length - 2)
              sentence_ids.append(sample_ids.asnumpy())
          avg_nll_loss += sum(
              [loss.as_in_ctx(mx.cpu()) for loss in loss_l]
          )
          mx.npx.waitall()
      avg_loss = avg_nll_loss.asnumpy() / ntokens
      pred_lengths = np.array(pred_lengths)
      sentence_ids = np.concatenate(sentence_ids, axis=0)
      return (
          avg_loss,
          ntokens,
          pred_sentences,
          pred_lengths,
          sentence_ids,
      )
  
imports: import argparse
  import time
  import random
  import os
  import logging
  import json
  import math
  import numpy
  import mxnet
  import sacrebleu
  from mxnet import gluon
  from gluonnlp.models.transformer import TransformerModel, TransformerInference
  from gluonnlp.sequence_sampler import BeamSearchSampler, BeamSearchScorer
  from gluonnlp.utils.misc import logging_config, md5sum, grouper, init_comm, repeat
  from gluonnlp.data.sampler import ConstWidthBucket, LinearWidthBucket, ExpWidthBucket, FixedBucketSampler, BoundedBudgetSampler, ShardedIterator
  from tensorboardX import SummaryWriter
  import gluonnlp.data.batchify
  from gluonnlp.data import Vocab
  from gluonnlp.data import tokenizers
  from gluonnlp.data.tokenizers import BaseTokenizerWithVocab, huggingface, MosesTokenizer
  from gluonnlp.lr_scheduler import InverseSquareRootScheduler
  from gluonnlp.loss import LabelSmoothCrossEntropyLoss
  from gluonnlp.utils.parameter import grad_global_norm, clip_grad_global_norm, deduplicate_param_dict, count_parameters, AverageSGDTracker
label_desc: Use image node_id 2 for calling a global function with example usage: validation(model, data_loader, inference_model, sequence_sampler, tgt_tokenizer, ctx_l) and returns: avg_loss,  ntokens,  pred_sentences,  pred_lengths,  sentence_ids