code: def test_SeparateSpeech(
      config_file,
      batch_size,
      input_size,
      segment_size,
      hop_size,
      normalize_segment_scale,
  ):
      separate_speech = SeparateSpeech(
          train_config=config_file,
          segment_size=segment_size,
          hop_size=hop_size,
          normalize_segment_scale=normalize_segment_scale,
      )
      wav = torch.rand(batch_size, input_size)
      separate_speech(wav, fs=8000)
  
imports: import string
  from argparse import ArgumentParser
  from pathlib import Path
  import pytest
  import torch
  import yaml
  from espnet2.bin.enh_inference import SeparateSpeech, get_parser, main
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.tasks.enh import EnhancementTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet2.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
label_desc: Use image node_id 4 for calling a global function with example usage: test_SeparateSpeech(config_file, batch_size, input_size, segment_size, hop_size, normalize_segment_scale) without return types