docstr: Feed tensors with specific shape to HDemucs Decoder and validate
  that it outputs with a tensor with expected shape for frequency domain.
docstr_tok: ["Feed","tensors","with","specific","shape","to","HDemucs","Decoder","and","validate","that","it","outputs","with","a","tensor","with","expected","shape","for","frequency","domain","."]
code: def test_decoder_output_shape_frequency(self):
      r"""Feed tensors with specific shape to HDemucs Decoder and validate
      that it outputs with a tensor with expected shape for frequency domain.
      """
      batch_size = 1
      chin, chout = 96, 48
      f_bins = 128
      t = 800
      stride = 4
  
      model = _HDecLayer(chin, chout).to(self.device).eval()
  
      x = torch.rand(
          batch_size,
          chin,
          f_bins,
          t,
          device=self.device,
          dtype=self.dtype,
      )
      skip = torch.rand(
          batch_size,
          chin,
          f_bins,
          t,
          device=self.device,
          dtype=self.dtype,
      )
      z, y = model(x, skip, t)
  
      assert z.size() == (batch_size, chout, f_bins * stride, t)
      assert y.size() == (batch_size, chin, f_bins, t)
  
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 3 for calling the HDemucsTests obj's underlying member method code with example usage: obj.test_decoder_output_shape_frequency() without return types