code: def run(train, test, validation, ranomd_state, model_type):
      results = Results("{}_results.txt".format(model_type))
  
      best_result = results.best()
  
      if model_type == "pooling":
          eval_fnc, sample_fnc = (
              evaluate_pooling_model,
              sample_pooling_hyperparameters,
          )
      elif model_type == "cnn":
          eval_fnc, sample_fnc = (
              evaluate_cnn_model,
              sample_cnn_hyperparameters,
          )
      elif model_type == "lstm":
          eval_fnc, sample_fnc = (
              evaluate_lstm_model,
              sample_lstm_hyperparameters,
          )
      else:
          raise ValueError("Unknown model type")
  
      if best_result is not None:
          print("Best {} result: {}".format(model_type, results.best()))
  
      for hyperparameters in sample_fnc(random_state, NUM_SAMPLES):
          if hyperparameters in results:
              continue
  
          print("Evaluating {}".format(hyperparameters))
  
          (test_mrr, val_mrr) = eval_fnc(
              hyperparameters, train, test, validation, random_state
          )
  
          print(
              "Test MRR {} val MRR {}".format(
                  test_mrr.mean(), val_mrr.mean()
              )
          )
  
          results.save(hyperparameters, test_mrr.mean(), val_mrr.mean())
  
      return results
  
imports: import hashlib
  import json
  import os
  import shutil
  import sys
  import numpy
  from sklearn.model_selection import ParameterSampler
  from spotlight.datasets.movielens import get_movielens_dataset
  from spotlight.cross_validation import user_based_train_test_split
  from spotlight.sequence.implicit import ImplicitSequenceModel
  from spotlight.sequence.representations import CNNNet
  from spotlight.evaluation import sequence_mrr_score
label_desc: Use image node_id 7 for calling a global function with example usage: run(train, test, validation, ranomd_state, model_type) and returns: results