code: def __init__(
      self,
      rate=_DEFAULTS["rate"],
      mixing_noise_rate=_DEFAULTS["mixing_noise_rate"],
      noise_path=_DEFAULTS["noise_path"],
      noise_snr_min=_DEFAULTS["noise_snr_min"],
      noise_snr_max=_DEFAULTS["noise_snr_max"],
      utterance_snr_min=_DEFAULTS["utterance_snr_min"],
      utterance_snr_max=_DEFAULTS["utterance_snr_max"],
  ):
      self.rate = rate
      self.mixing_noise_rate = mixing_noise_rate
      self.noise_shaper = NoiseAugmentTransform(noise_path)
      self.noise_snr_min = noise_snr_min
      self.noise_snr_max = noise_snr_max
      self.utterance_snr_min = utterance_snr_min
      self.utterance_snr_max = utterance_snr_max
  
file_path: fairseq/fairseq/data/audio/dataset_transforms/noisyoverlapaugment.py
imports: import numpy
  import torch
  from fairseq.data.audio import rand_uniform
  from fairseq.data.audio.dataset_transforms import AudioDatasetTransform, register_audio_dataset_transform
  from fairseq.data.audio.waveform_transforms.noiseaugment import NoiseAugmentTransform
label_desc: Use image node_id 2 to create a new NoisyOverlapAugment object from inherited base classes: AudioDatasetTransform with example: obj = NoisyOverlapAugment(rate, mixing_noise_rate, noise_path, noise_snr_min, noise_snr_max, utterance_snr_min, utterance_snr_max)