code: def evaluation(writer, step_num, masked_input, eval_input):
      length_masks = masked_input.masks
      unmasked_tokens = masked_input.unmasked_tokens
      masked_weights = masked_input.masked_weights
      mlm_scores = eval_input.mlm_scores
      rtd_scores = eval_input.rtd_scores
      rtd_labels = eval_input.rtd_labels
      corrupted_tokens = eval_input.corrupted_tokens
  
      mlm_log_probs = mx.npx.log_softmax(mlm_scores)
      mlm_preds = mx.np.argmax(mlm_log_probs, axis=-1).astype(np.int32)
      rtd_probs = mx.npx.sigmoid(rtd_scores)
      rtd_preds = mx.np.round((mx.np.sign(rtd_scores) + 1) / 2).astype(
          np.int32
      )
  
      mlm_accuracy = accuracy(
          unmasked_tokens, mlm_preds, masked_weights
      )
      corrupted_mlm_accuracy = accuracy(
          unmasked_tokens, corrupted_tokens, masked_weights
      )
      rtd_accuracy = accuracy(rtd_labels, rtd_preds, length_masks)
      rtd_precision = accuracy(
          rtd_labels, rtd_preds, length_masks * rtd_preds
      )
      rtd_recall = accuracy(
          rtd_labels, rtd_preds, rtd_labels * rtd_preds
      )
      rtd_auc = auc(rtd_labels, rtd_probs, length_masks)
      logging.info(
          "Eval [step {}], mlm_accuracy={:.4f}, corrupted_mlm_accuracy={:.4f},"
          " rtd_accuracy={:.4f}, rtd_precision={:.4f}, rtd_recall={:.4f},"
          " rtd_auc={:.4f}.".format(
              step_num,
              mlm_accuracy,
              corrupted_mlm_accuracy,
              rtd_accuracy,
              rtd_precision,
              rtd_recall,
              rtd_auc,
          )
      )
      if writer is not None:
          writer.add_scalars(
              "results",
              {
                  "mlm_accuracy": mlm_accuracy,
                  "corrupted_mlm_accuracy": corrupted_mlm_accuracy,
                  "rtd_accuracy": rtd_accuracy,
                  "rtd_precision": rtd_precision,
                  "rtd_recall": rtd_recall,
                  "rtd_auc": rtd_auc,
              },
              step_num,
          )
  
imports: import os
  import time
  import shutil
  import logging
  import argparse
  import functools
  import collections
  import mxnet
  import numpy
  from mxnet.lr_scheduler import PolyScheduler
  from sklearn import metrics
  from pretraining_utils import ElectraMasker, get_pretrain_data_npz, get_pretrain_data_text, get_electra_pretraining_model
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, naming_convention
  from gluonnlp.models.electra import ElectraModel, ElectraForPretrain
  from gluonnlp.utils.parameter import clip_grad_global_norm
label_desc: Use image node_id 8 for calling a global function with example usage: evaluation(writer, step_num, masked_input, eval_input) without return types