code: def test_time_masking(self, input_shape):
      transform = T.TimeMasking(time_mask_param=5)
      # Genearte a specgram tensor containing 1's only, for the ease of testing.
      specgram = torch.ones(*input_shape)
      masked = transform(specgram)
      dim = len(input_shape)
      # Across the axis (dim-1) where we apply masking,
      # the mean tensor should contain equal elements,
      # and the value should be between 0 and 1.
      m_masked = torch.mean(masked, dim - 1)
      self.assertEqual(torch.var(m_masked), 0)
      self.assertTrue(torch.mean(m_masked) > 0)
      self.assertTrue(torch.mean(m_masked) < 1)
  
      # Across all other dimensions, the mean tensor should contain at least
      # one zero element, and all non-zero elements should be 1.
      for axis in range(dim - 1):
          unmasked_axis_mean = torch.mean(masked, axis)
          self.assertTrue(0 in unmasked_axis_mean)
          self.assertFalse(
              False
              in torch.eq(
                  unmasked_axis_mean[unmasked_axis_mean != 0], 1
              )
          )
  
imports: import math
  import random
  from unittest.mock import patch
  import numpy
  import torch
  import torchaudio.transforms
  from parameterized import param, parameterized
  from scipy import signal
  from torchaudio.functional import lfilter, preemphasis
  from torchaudio.functional.functional import _get_sinc_resample_kernel
  from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
  from torchaudio_unittest.common_utils.psd_utils import psd_numpy
label_desc: Use image node_id 18 for calling the TransformsTestBase obj's underlying member method code with example usage: obj.test_time_masking(input_shape) without return types