code: def test_init_parameters_default(self):
      vocab_size = 1000
      max_seq_length = 50
  
      model = KimCNN(vocab_size, max_seq_length)
  
      pool_sizes = [(47, 1), (46, 1), (45, 1)]
  
      # Parameters
      self.assertEqual(100, model.out_channels)
      self.assertEqual(1, model.in_channels)
      self.assertEqual(3, model.num_kernels)
      self.assertEqual(pool_sizes, model.pool_sizes)
      self.assertEqual(max_seq_length, model.max_seq_length)
      self.assertEqual(2, model.num_classes)
  
      # Modules
      self.assertTrue(model.embedding.weight.requires_grad)
  
      self.assertEqual(0, model.embedding.padding_idx)
      self.assertEqual(vocab_size, model.embedding.num_embeddings)
      self.assertEqual(300, model.embedding.embedding_dim)
  
      self.assertEqual(len(pool_sizes), len(model.pools))
      for i, pool in enumerate(model.pools):
          self.assertEqual(pool_sizes[i], pool.kernel_size)
  
      self.assertEqual(3, len(model.convs))
      for i, conv in enumerate(model.convs):
          self.assertEqual(1, conv.in_channels)
          self.assertEqual(100, conv.out_channels)
          self.assertEqual(
              (self.DEFAULT_KERNEL_HEIGHTS[i], 300), conv.kernel_size
          )
  
      self.assertEqual(0.5, model.dropout.p)
      self.assertEqual(300, model.fc.in_features)
      self.assertEqual(2, model.fc.out_features)
  
imports: import unittest
  import pytest
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
label_desc: Use image node_id 1 for calling the KimCNNInitTest obj's underlying member method code with example usage: obj.test_init_parameters_default() without return types