code: def test_conv1d(self):
      inputs = torch.rand(
          [self.batch_size, self.in_channels, self.max_len]
      )
      lengths = torch.tensor([self.len1, self.len2], dtype=torch.int32)
  
      conv = Conv1dNorm(
          in_channels=self.in_channels,
          out_channels=self.out_channels,
          kernel_size=self.kernel_size,
      )
      out = conv(inputs=inputs, input_len=lengths)
  
      assert out.shape == (
          self.batch_size,
          self.out_channels,
          self.max_len,
      )
      assert torch.all(out[0, :, : self.len1] != 0.0)
      assert torch.all(out[0, :, self.len1 :] == 0.0)
      assert torch.all(out[1, :, : self.len2] != 0.0)
      assert torch.all(out[1, :, self.len2 :] == 0.0)
  
imports: import pytest
  import torch
  from nemo.collections.tts.modules.audio_codec_modules import CodecActivation, Conv1dNorm, ConvTranspose1dNorm, FiniteScalarQuantizer, GroupFiniteScalarQuantizer, get_down_sample_padding
  from nemo.collections.tts.modules.encodec_modules import GroupResidualVectorQuantizer, ResidualVectorQuantizer
label_desc: Use image node_id 2 for calling the TestAudioCodecModules obj's underlying member method code with example usage: obj.test_conv1d() without return types