code: def create_mlp_generator(
      im_size, n_chan, n_feature, depth, batch_norm, finact
  ):
      """
      Create MLP generator network
  
      Arguments:
          im_size (int): Image size
          n_chan (int): Number of color channels
          n_feature (int): Base number of features
          depth (int): Depth of network
          batch_norm(bool): Enable batch normalization
          finact (object): Final activation function
      """
      assert depth > 1, "depth of the MLP has to be at least 2"
      layers = []
      for i in range(depth - 1):
          lname = "gen-{0}.{1}".format(i, n_feature)
          layers.append(
              mlp_layer(lname, n_feature, batch_norm=batch_norm)
          )
      lname = "gen-{0}.{1}-{2}".format(
          depth - 1, n_feature, n_chan * im_size * im_size
      )
      layers.append(
          mlp_layer(
              lname,
              n_chan * im_size * im_size,
              activation=finact,
              batch_norm=batch_norm,
          )
      )
      lname = "gen-final"
      layers.append(
          Reshape(name=lname, reshape=(n_chan, im_size, im_size))
      )
      return layers
  
file_path: neon/examples/gan/network_gan.py
imports: from neon.initializers import Gaussian
  from neon.layers import Sequential, Reshape, Convolution, BatchNorm, Activation, Deconvolution, Linear, GeneralizedGANCost, Bias
  from neon.transforms import Rectlin, Tanh, Logistic, Identity
  from neon.layers.container import GenerativeAdversarial
  from neon.models.model import GAN
  from neon.transforms.cost import GANCost
label_desc: Use image node_id 5 for calling a global function with example usage: create_mlp_generator(im_size, n_chan, n_feature, depth, batch_norm, finact) and returns: layers