code: def test_class_init(init_rbm):
      model = RBM(
          possible_ratings=init_rbm["possible_ratings"],
          visible_units=init_rbm["n_visible"],
          hidden_units=init_rbm["n_hidden"],
          training_epoch=init_rbm["epochs"],
          minibatch_size=init_rbm["minibatch"],
          keep_prob=init_rbm["keep_prob"],
          learning_rate=init_rbm["learning_rate"],
          init_stdv=init_rbm["init_stdv"],
          sampling_protocol=init_rbm["sampling_protocol"],
          display_epoch=init_rbm["display_epoch"],
      )
  
      # list of unique rating values
      assert np.array_equal(
          model.possible_ratings, init_rbm["possible_ratings"]
      )
      # number of visible units
      assert model.n_visible == init_rbm["n_visible"]
      # number of hidden units
      assert model.n_hidden == init_rbm["n_hidden"]
      # number of training epochs
      assert model.epochs == init_rbm["epochs"] + 1
      # minibatch size
      assert model.minibatch == init_rbm["minibatch"]
      # keep probability for dropout regulrization
      assert model.keep == init_rbm["keep_prob"]
      # learning rate
      assert model.learning_rate == init_rbm["learning_rate"]
      # standard deviation used to initialize the weight matrix from a normal distribution
      assert model.stdv == init_rbm["init_stdv"]
      # sampling protocol used to increase the number of steps in Gibbs sampling
      assert model.sampling_protocol == init_rbm["sampling_protocol"]
      # number of epochs after which the rmse is displayed
      assert model.display_epoch == init_rbm["display_epoch"]
  
imports: import pytest
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: test_class_init(init_rbm) without return types