code: def test_masked_layer_norm(self):
      x_n = np.random.rand(2, 3, 7)
      mask_n = np.array([[1, 1, 0], [1, 1, 1]])
  
      x = torch.from_numpy(x_n).float()
      mask = torch.from_numpy(mask_n).bool()
  
      layer_norm = MaskedLayerNorm(7, gamma0=0.2)
      normed_x = layer_norm(x, mask)
  
      N = 7 * 5
      mean = (x_n * np.expand_dims(mask_n, axis=-1)).sum() / N
      std = np.sqrt(
          (((x_n - mean) * np.expand_dims(mask_n, axis=-1)) ** 2).sum()
          / N
          + util.tiny_value_of_dtype(torch.float)
      )
      expected = (
          0.2
          * (x_n - mean)
          / (std + util.tiny_value_of_dtype(torch.float))
      )
  
      assert np.allclose(normed_x.data.numpy(), expected)
  
file_path: allennlp/tests/modules/masked_layer_norm_test.py
imports: import numpy
  import torch
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.modules.masked_layer_norm import MaskedLayerNorm
  from allennlp.nn import util
label_desc: Use image node_id 1 for calling the TestMaskedLayerNorm obj's underlying member method code with example usage: obj.test_masked_layer_norm() without return types