code: def test_mlp(self):
      mlp = layers.ClassifierLayer("mlp", in_dim=300, out_dim=1)
      self.assertEqual(len(list(mlp.module.layers.children())), 1)
      self.assertEqual(len(list(mlp.parameters())), 2)
  
      inp = torch.rand(3, 300)
  
      output = mlp(inp)
      self.assertEqual(output.size(), torch.Size((3, 1)))
      np.testing.assert_almost_equal(
          output.squeeze().tolist(), [0.1949174, 0.4030975, -0.0109139]
      )
  
      mlp = layers.ClassifierLayer(
          "mlp", in_dim=300, out_dim=1, hidden_dim=150, num_layers=1
      )
  
      self.assertEqual(len(list(mlp.module.layers.children())), 5)
      self.assertEqual(len(list(mlp.parameters())), 6)
  
      inp = torch.rand(3, 300)
  
      output = mlp(inp)
      self.assertEqual(output.size(), torch.Size((3, 1)))
      np.testing.assert_almost_equal(
          output.squeeze().tolist(),
          [-0.503411, 0.1725615, -0.6833304],
          decimal=3,
      )
  
file_path: mmf/tests/modules/test_layers.py
imports: import functools
  import operator
  import random
  import unittest
  import mmf.modules.layers
  import numpy
  import torch
  from omegaconf import OmegaConf
label_desc: Use image node_id 5 for calling the TestModuleLayers obj's underlying member method code with example usage: obj.test_mlp() without return types