code: def test_HuBERT_(
      float_pad_value,
      int_pad_value,
      not_sequence,
      label_downsampling,
      pad,
      rand_crop,
      window_size,
      window_shift,
      sample_rate,
  ):
      _hubert_collate_fn = HuBERTCollateFn(
          float_pad_value=float_pad_value,
          int_pad_value=int_pad_value,
          not_sequence=not_sequence,
          label_downsampling=label_downsampling,
          pad=pad,
          rand_crop=rand_crop,
          window_size=window_size,
          window_shift=window_shift,
          sample_rate=sample_rate,
      )
      data = [
          (
              "id",
              dict(
                  speech=np.random.randn(16000),
                  text=np.random.randn(49).astype(np.int64),
              ),
          ),
          (
              "id2",
              dict(
                  speech=np.random.randn(22000),
                  text=np.random.randn(67).astype(np.int64),
              ),
          ),
      ]
      t = _hubert_collate_fn(data)
  
      if pad:
          desired = dict(
              speech=np.stack(
                  [
                      np.pad(
                          data[0][1]["speech"],
                          (0, 6000),
                          mode="constant",
                          constant_values=float_pad_value,
                      ),
                      data[1][1]["speech"],
                  ]
              ),
              text=np.stack(
                  [
                      np.pad(
                          data[0][1]["text"],
                          (0, 18),
                          mode="constant",
                          constant_values=int_pad_value,
                      )[::label_downsampling],
                      data[1][1]["text"][::label_downsampling],
                  ]
              ),
              speech_lengths=np.array([16000, 22000], dtype=np.int64),
              text_lengths=np.array([49, 67], dtype=np.int64),
          )
      else:
          desired = dict(
              speech=np.stack(
                  [
                      data[0][1]["speech"],
                      data[1][1]["speech"][:16000],
                  ]
              ),
              text=np.stack(
                  [
                      data[0][1]["text"][::label_downsampling],
                      data[1][1]["text"][:49:label_downsampling],
                  ]
              ),
              speech_lengths=np.array([16000, 16000], dtype=np.int64),
              text_lengths=np.array([49, 49], dtype=np.int64),
          )
  
      if label_downsampling > 1:
          desired["text_lengths"] = (
              desired["text_lengths"] + 1 - label_downsampling
          ) // label_downsampling + 1
  
      np.testing.assert_array_equal(t[1]["speech"], desired["speech"])
      np.testing.assert_array_equal(t[1]["text"], desired["text"])
  
      if "speech" not in not_sequence:
          np.testing.assert_array_equal(
              t[1]["speech_lengths"], desired["speech_lengths"]
          )
      if "text" not in not_sequence:
          np.testing.assert_array_equal(
              t[1]["text_lengths"], desired["text_lengths"]
          )
  
imports: import numpy
  import pytest
  from espnet2.train.collate_fn import CommonCollateFn, HuBERTCollateFn, common_collate_fn
label_desc: Use image node_id 4 for calling a global function with example usage: test_HuBERT_(float_pad_value, int_pad_value, not_sequence, label_downsampling, pad, rand_crop, window_size, window_shift, sample_rate) without return types