code: def test_flatten(self):
      flatten = layers.Flatten()
  
      # Test 3 dim
      input_tensor = torch.randn(5, 6, 10)
      expected_size = torch.Size((5, 60))
      actual_size = flatten(input_tensor).size()
      self.assertEqual(actual_size, expected_size)
  
      # Test 1 dim
      input_tensor = torch.randn(5)
      expected_size = torch.Size((5,))
      actual_size = flatten(input_tensor).size()
      self.assertEqual(actual_size, expected_size)
  
      # Test 6 dim
      size_list = [random.randint(2, 4) for _ in range(7)]
      expected_size = torch.Size(
          (size_list[0], functools.reduce(operator.mul, size_list[1:]))
      )
      input_tensor = torch.randn(*size_list)
      actual_size = flatten(input_tensor).size()
      self.assertEqual(actual_size, expected_size)
  
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 3 for calling the TestModuleLayers obj's underlying member method code with example usage: obj.test_flatten() without return types