code: def pick_sample(
      self, goal_shape, always_2d=False, use_sample_rate=None
  ):
      from fairseq.data.audio.audio_utils import get_waveform
  
      path = self.paths[np.random.randint(0, self.n_samples)]
      sample = get_waveform(
          path, always_2d=always_2d, output_sample_rate=use_sample_rate
      )[0]
  
      # Check dimensions match, else silently skip adding noise to sample
      # NOTE: SHOULD THIS QUIT WITH AN ERROR?
      is_2d = len(goal_shape) == 2
      if len(goal_shape) != sample.ndim or (
          is_2d and goal_shape[0] != sample.shape[0]
      ):
          return np.zeros(goal_shape)
  
      # Cut/repeat sample to size
      len_dim = len(goal_shape) - 1
      n_repeat = ceil(goal_shape[len_dim] / sample.shape[len_dim])
      repeated = np.tile(sample, [1, n_repeat] if is_2d else n_repeat)
      start = np.random.randint(
          0, repeated.shape[len_dim] - goal_shape[len_dim] + 1
      )
      return (
          repeated[:, start : start + goal_shape[len_dim]]
          if is_2d
          else repeated[start : start + goal_shape[len_dim]]
      )
  
imports: from pathlib import Path
  import numpy
  from math import ceil
  from fairseq.data.audio import rand_uniform
  from fairseq.data.audio.waveform_transforms import AudioWaveformTransform, register_audio_waveform_transform
label_desc: Use image node_id 4 for calling the NoiseAugmentTransform obj's underlying member method code with example usage: obj.pick_sample(goal_shape, always_2d, use_sample_rate) and returns: unknown, np