code: def mlp(in_size, hidden_size, n_layers):
      channels = [in_size] + (n_layers) * [hidden_size]
      net = []
      for i in range(n_layers):
          net.append(nn.Linear(channels[i], channels[i + 1]))
          net.append(nn.LayerNorm(channels[i + 1]))
          net.append(nn.LeakyReLU())
      return nn.Sequential(*net)
  
imports: import math
  import librosa
  import numpy
  import torch
  import torch.fft
  import torch.nn
  from scipy.signal import get_window
label_desc: Use image node_id 10 for calling a global function with example usage: mlp(in_size, hidden_size, n_layers) and returns: nn