code: def mono_to_color(X, eps=1e-6, mean=None, std=None):
      X = np.stack([X, X, X], axis=-1)
      # Standardize
      mean = mean or X.mean()
      std = std or X.std()
      X = (X - mean) / (std + eps)
      # Normalize to [0, 255]
      _min, _max = X.min(), X.max()
      if (_max - _min) > eps:
          V = np.clip(X, _min, _max)
          V = 255 * (V - _min) / (_max - _min)
          V = V.astype(np.uint8)
      else:
          V = np.zeros_like(X, dtype=np.uint8)
      return V
  
file_path: gradio/demo/musical_instrument_identification/data_setups.py
imports: import os
  import librosa
  import torch
  import numpy
  from torchaudio.transforms import Resample
label_desc: Use image node_id 3 for calling a global function with example usage: mono_to_color(X, eps, mean, std) and returns: V