code: def test_pooling(self):
      # Setup embedding
      config = IntWeightedMultiCategoryEmbedding.Config(
          pooling_type="max", feature_buckets={1: 100, 2: 200}
      )
      embedding_module = IntWeightedMultiCategoryEmbedding.from_config(
          config
      )
  
      output = embedding_module(
          {
              1: (
                  torch.tensor([11, 12, 13], dtype=torch.int),
                  torch.tensor([0, 1], dtype=torch.int),
                  torch.tensor([0.0, 0.0, 0.0]),
              ),
              2: (
                  torch.tensor([1], dtype=torch.int),
                  torch.tensor([0, 0], dtype=torch.int),
                  torch.tensor([1.0]),
              ),
          }
      )
  
      self.assertEqual(list(output.size()), [2, 32])
      self.assertEqual(embedding_module.get_output_dim(), 32)
  
imports: import unittest
  import torch
  from pytext.models.embeddings.int_weighted_multi_category_embedding import IntWeightedMultiCategoryEmbedding
label_desc: Use image node_id 2 for calling the IntWeightedMultiCategoryEmbeddingTest obj's underlying member method code with example usage: obj.test_pooling() without return types