code: def test_basic(self):
      # Setup embedding
      output_dim = 16
      embedding_module = MLPEmbedding(
          embedding_dim=4,
          embeddings_weight=None,
          init_range=[-1, 1],
          mlp_layer_dims=[output_dim],
      )
      self.assertEqual(embedding_module.embedding_dim, output_dim)
  
      # Check output shape
      input_batch_size, input_dim = 4, 4
      dense_features = torch.rand(size=[input_batch_size, input_dim])
      output_embedding = embedding_module(dense_features)
      expected_output_dims = [input_batch_size, output_dim]
      self.assertEqual(
          list(output_embedding.size()), expected_output_dims
      )
  
imports: import unittest
  import torch
  from pytext.models.embeddings.mlp_embedding import MLPEmbedding
label_desc: Use image node_id 1 for calling the MLPEmbeddingTest obj's underlying member method code with example usage: obj.test_basic() without return types