code: def evaluate(
      experiments: Iterable[Tuple[Iterable[SlateEstimator], int]],
      dataset: MSLRDatasets,
      slate_size: int,
      item_size: int,
      metric_func: str,
      log_trainer: Trainer,
      log_distribution: RewardDistribution,
      log_features: str,
      tgt_trainer: Trainer,
      tgt_distribution: RewardDistribution,
      tgt_features: str,
      dm_features: str,
      max_num_workers: int,
      device=None,
  ):
      assert slate_size < item_size
      print(
          f"Evaluate All:"
          f" slate_size={slate_size}, item_size={item_size}, metric={metric_func}"
          f", Log=[{log_trainer.name}, {log_distribution}, {log_features}]"
          f", Target=[{tgt_trainer.name}, {tgt_distribution}, {tgt_features}]"
          f", DM=[{dm_features}]"
          f", Workers={max_num_workers}, device={device}",
          flush=True,
      )
      logging.info("Preparing models and policies...")
      st = time.perf_counter()
      log_trainer.load_model(
          os.path.join(
              dataset.folder,
              log_trainer.name + "_all_" + log_features + ".pickle",
          )
      )
      # calculate behavior model scores
      log_pred = log_trainer.predict(getattr(dataset, log_features))
  
      tgt_trainer.load_model(
          os.path.join(
              dataset.folder,
              tgt_trainer.name + "_all_" + tgt_features + ".pickle",
          )
      )
      # calculate target model scores
      tgt_pred = tgt_trainer.predict(getattr(dataset, tgt_features))
  
      dm_train_features = getattr(dataset, dm_features)
  
      slots = SlateSlots(slate_size)
  
      dt = time.perf_counter() - st
      logging.info(f"Preparing models and policies done: {dt}s")
  
      total_samples = 0
      for _, num_samples in experiments:
          total_samples += num_samples
      logging.info(f"Generating log: total_samples={total_samples}")
      st = time.perf_counter()
      tasks = []
      samples_generated = 0
      total_queries = dataset.queries.shape[0]
      for estimators, num_samples in experiments:
          samples = []
          for _ in range(num_samples):
              # randomly sample a query
              q = dataset.queries[random.randrange(total_queries)]
              doc_size = int(q[2])
              if doc_size < item_size:
                  # skip if number of docs is less than item_size
                  continue
              si = int(q[1])
              ei = si + doc_size
              # using top item_size docs for logging
              log_scores, item_choices = log_pred.scores[si:ei].sort(
                  dim=0, descending=True
              )
              log_scores = log_scores[:item_size]
              item_choices = item_choices[:item_size]
              log_item_probs = log_distribution(
                  SlateItemValues(log_scores)
              )
              tgt_scores = (
                  tgt_pred.scores[si:ei][item_choices].detach().clone()
              )
              tgt_item_probs = tgt_distribution(
                  SlateItemValues(tgt_scores)
              )
              tgt_slot_expectation = (
                  tgt_item_probs.slot_item_expectations(slots)
              )
              gt_item_rewards = SlateItemValues(
                  dataset.relevances[si:ei][item_choices]
              )
              gt_rewards = tgt_slot_expectation.expected_rewards(
                  gt_item_rewards
              )
              if metric_func == "dcg":
                  metric = DCGSlateMetric(device=device)
              elif metric_func == "err":
                  metric = ERRSlateMetric(4.0, device=device)
              else:
                  metric = NDCGSlateMetric(
                      gt_item_rewards, device=device
                  )
              query = SlateQuery((si, ei))
              context = SlateContext(query, slots, item_choices)
              slot_weights = metric.slot_weights(slots)
              gt_reward = metric.calculate_reward(
                  slots, gt_rewards, None, slot_weights
              )
              if tgt_item_probs.is_deterministic:
                  tgt_slate_prob = 1.0
                  log_slate = tgt_item_probs.sample_slate(slots)
                  log_reward = gt_reward
              else:
                  tgt_slate_prob = float("nan")
                  log_slate = log_item_probs.sample_slate(slots)
                  log_rewards = log_slate.slot_values(gt_item_rewards)
                  log_reward = metric.calculate_reward(
                      slots, log_rewards, None, slot_weights
                  )
              log_slate_prob = log_item_probs.slate_probability(
                  log_slate
              )
              item_features = SlateItemFeatures(
                  dm_train_features[si:ei][item_choices]
              )
              sample = LogSample(
                  context,
                  metric,
                  log_slate,
                  log_reward,
                  log_slate_prob,
                  None,
                  log_item_probs,
                  tgt_slate_prob,
                  None,
                  tgt_item_probs,
                  gt_reward,
                  slot_weights,
                  None,
                  item_features,
              )
              samples.append(sample)
              samples_generated += 1
              if samples_generated % 1000 == 0:
                  logging.info(
                      f"  samples generated: {samples_generated}, {100 * samples_generated / total_samples:.1f}%"
                  )
          tasks.append((estimators, SlateEstimatorInput(samples)))
      dt = time.perf_counter() - st
      logging.info(
          f"Generating log done: {total_samples} samples in {dt}s"
      )
  
      logging.info("start evaluating...")
      st = time.perf_counter()
      evaluator = Evaluator(tasks, max_num_workers)
      Evaluator.report_results(evaluator.evaluate())
      logging.info(f"evaluating done in {time.perf_counter() - st}s")
  
imports: import argparse
  import itertools
  import json
  import logging
  import os
  import pickle
  import random
  import sys
  import time
  from collections import OrderedDict
  from typing import Iterable, List, Optional, Tuple
  import numpy
  import torch
  import torch.multiprocessing
  from reagent.ope.estimators.estimator import Evaluator
  from reagent.ope.estimators.slate_estimators import DCGSlateMetric, DMEstimator, DoublyRobustEstimator, ERRSlateMetric, IPSEstimator, LogSample, NDCGSlateMetric, PassThruDistribution, PBMEstimator, PseudoInverseEstimator, RankingDistribution, RewardDistribution, SlateContext, SlateEstimator, SlateEstimatorInput, SlateItemFeatures, SlateItemValues, SlateModel, SlateQuery, SlateSlots
  from reagent.ope.estimators.types import Trainer, TrainingData
  from reagent.ope.trainers.linear_trainers import DecisionTreeTrainer, LassoTrainer
  from reagent.ope.utils import Clamper
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(experiments, dataset, slate_size, item_size, metric_func, log_trainer, log_distribution, log_features, tgt_trainer, tgt_distribution, tgt_features, dm_features, max_num_workers, device) without return types