docstr: Feed tensors with specific shape to HDemucs and validate
  that it outputs with a tensor with expected shape.
docstr_tok: ["Feed","tensors","with","specific","shape","to","HDemucs","and","validate","that","it","outputs","with","a","tensor","with","expected","shape","."]
code: def test_hdemucs_output_shape(self, sources, nfft_bundle):
      r"""Feed tensors with specific shape to HDemucs and validate
      that it outputs with a tensor with expected shape.
      """
      duration = 10
      channels = 2
      batch_size = 1
      sample_rate = 44100
      nfft = nfft_bundle[0]
      depth = nfft_bundle[1]
  
      model = (
          _get_hdemucs_model(sources, nfft, depth)
          .to(self.device)
          .eval()
      )
      inputs = _get_inputs(
          sample_rate, self.device, batch_size, duration, channels
      )
  
      split_sample = model(inputs)
  
      assert split_sample.shape == (
          batch_size,
          len(sources),
          channels,
          duration * sample_rate,
      )
  
imports: import itertools
  from typing import List
  import torch
  from parameterized import parameterized
  from torchaudio.models._hdemucs import _HDecLayer, _HEncLayer, HDemucs, hdemucs_high, hdemucs_low
  from torchaudio_unittest.common_utils import skipIfNoModule, TestBaseMixin, TorchaudioTestCase
label_desc: Use image node_id 1 for calling the HDemucsTests obj's underlying member method code with example usage: obj.test_hdemucs_output_shape(sources, nfft_bundle) without return types