code: def sample_hyperparameters(random_state, num):
      space = {
          "n_iter": N_ITER,
          "batch_size": BATCH_SIZE,
          "l2": L2,
          "learning_rate": LEARNING_RATES,
          "loss": LOSSES,
          "embedding_dim": EMBEDDING_DIM,
      }
  
      sampler = ParameterSampler(
          space, n_iter=num, random_state=random_state
      )
  
      for params in sampler:
          yield params
  
imports: import argparse
  import hashlib
  import json
  import os
  import shutil
  import time
  import numpy
  from sklearn.model_selection import ParameterSampler
  from spotlight.datasets.movielens import get_movielens_dataset
  from spotlight.datasets.amazon import get_amazon_dataset
  from spotlight.cross_validation import random_train_test_split, user_based_train_test_split
  from spotlight.sequence.implicit import ImplicitSequenceModel
  from spotlight.factorization.implicit import ImplicitFactorizationModel
  from spotlight.sequence.representations import LSTMNet
  from spotlight.factorization.representations import BilinearNet
  from spotlight.layers import BloomEmbedding, ScaledEmbedding
  from spotlight.evaluation import mrr_score, sequence_mrr_score
  from spotlight.torch_utils import set_seed
label_desc: Use image node_id 1 for calling a global function with example usage: sample_hyperparameters(random_state, num) without return types