code: def test_init_default_parameters(self):
      num_classes = 2
      embedding_matrix = np.random.rand(10, 100)
      classifier = KimCNNClassifier(
          num_classes, embedding_matrix=embedding_matrix
      )
  
      self.assertEqual(num_classes, classifier.num_classes)
      self.assertFalse(classifier.multi_label)
      self.assertIsNotNone(classifier.device)
      self.assertEqual(10, classifier.num_epochs)
      self.assertEqual(25, classifier.mini_batch_size)
      self.assertIsNone(classifier.criterion)
      self.assertIsNone(classifier.optimizer)
      self.assertIsNone(classifier.scheduler)
      self.assertEqual(0.001, classifier.lr)
      self.assertEqual(60, classifier.max_seq_len)
      self.assertEqual(100, classifier.out_channels)
      self.assertEqual(0, classifier.filter_padding)
      self.assertEqual(0.5, classifier.dropout)
      self.assertEqual(0.1, classifier.validation_set_size)
      np.testing.assert_equal(
          embedding_matrix, classifier.embedding_matrix
      )
      self.assertEqual(0, classifier.padding_idx)
      self.assertEqual([3, 4, 5], classifier.kernel_heights)
      self.assertEqual(5, classifier.early_stopping)
      self.assertEqual(-1, classifier.early_stopping_acc)
      self.assertIsNone(classifier.model)
  
imports: import unittest
  import pytest
  import numpy
  from unittest.mock import patch
  from small_text.base import LABEL_UNLABELED
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.training.metrics import Metric
  from small_text.training.early_stopping import EarlyStopping, EarlyStoppingOrCondition
label_desc: Use image node_id 2 for calling the KimCNNTest obj's underlying member method code with example usage: obj.test_init_default_parameters() without return types