code: def rosa_resample(input, in_rate, out_rate):
      if input.shape[1] == 1:
          return librosa.resample(
              input[:, 0], orig_sr=in_rate, target_sr=out_rate
          )[:, np.newaxis]
  
      channels = [
          librosa.resample(
              np.array(input[:, c]), orig_sr=in_rate, target_sr=out_rate
          )
          for c in range(input.shape[1])
      ]
      ret = np.zeros(
          shape=[channels[0].shape[0], len(channels)],
          dtype=channels[0].dtype,
      )
      for c, a in enumerate(channels):
          ret[:, c] = a
  
      return ret
  
imports: import numpy
  import math
  import librosa
label_desc: Use image node_id 2 for calling a global function with example usage: rosa_resample(input, in_rate, out_rate) and returns: ret, librosa