code: def evaluate(
      self, model, tensor_examples, stored_info, step, predict=False
  ):
      evaluator = EREEvaluator()
  
      eval_batch_size = 32
      if "pp" in self.name or "11b" in self.name or "xxl" in self.name:
          eval_batch_size = 24
      if "doclevel" in self.name:
          eval_batch_size = 4
      util.runner.logger.info(
          "Step %d: evaluating on %d samples with batch_size %d"
          % (step, len(tensor_examples), eval_batch_size)
      )
  
      evalloader = DataLoader(
          tensor_examples,
          batch_size=eval_batch_size,
          shuffle=False,
          num_workers=0,
          collate_fn=self.collate_fn,
          pin_memory=True,
      )
      model.eval()
      for i, (doc_keys, tensor_example) in enumerate(evalloader):
          example_gpu = {}
  
          for k, v in tensor_example.items():
              if v is not None:
                  example_gpu[k] = v.to(self.device)
          example_gpu["is_training"][:] = 0
  
          with torch.no_grad(), torch.cuda.amp.autocast(
              enabled=self.use_amp, dtype=torch.bfloat16
          ):
              output = model(**example_gpu)
  
          for batch_id, doc_key in enumerate(doc_keys):
              gold_res = model.extract_gold_res_from_gold_annotation(
                  {k: v[batch_id] for k, v in tensor_example.items()},
                  stored_info["example"][doc_key],
              )
              decoded_results = model.decoding(
                  {k: v[batch_id] for k, v in output.items()},
                  stored_info["example"][doc_key],
              )
  
              decoded_results.update(gold_res)
              evaluator.update(**decoded_results)
              if predict:
                  util.runner.logger.info(
                      stored_info["example"][doc_key]
                  )
                  util.runner.logger.info(decoded_results)
  
      p, r, f = evaluator.get_prf()
      metrics = {
          "Eval_Ent_Precision": p[0] * 100,
          "Eval_Ent_Recall": r[0] * 100,
          "Eval_Ent_F1": f[0] * 100,
          "Eval_Rel_Precision": p[1] * 100,
          "Eval_Rel_Recall": r[1] * 100,
          "Eval_Rel_F1": f[1] * 100,
          "Eval_Rel_p_Precision": p[2] * 100,
          "Eval_Rel_p_Recall": r[2] * 100,
          "Eval_Rel_p_F1": f[2] * 100,
      }
      for k, v in metrics.items():
          util.runner.logger.info("%s: %.4f" % (k, v))
  
      return f[1] * 100, metrics
  
imports: from deepke.triple_extraction.ASP import *
  import sys
  import logging
  import random
  import numpy
  import torch
  from torch.optim import AdamW
  import deepke.triple_extraction.ASP.util
  from deepke.triple_extraction.ASP.util.multigpu_fused_adam import FusedAdam
  from torch.optim.lr_scheduler import LambdaLR
  from torch.utils.data import DataLoader
  import time
  from os.path import join
  from datetime import datetime
  from deepke.triple_extraction.ASP.util.runner import Runner
  from metrics import EREEvaluator
  import wandb
  import hydra
label_desc: Use image node_id 1 for calling the ERERunner obj's underlying member method code with example usage: obj.evaluate(model, tensor_examples, stored_info, step, predict) and returns: unknown,  metrics