code: def mlp_layer(
      name, nout, activation=relu, batch_norm=False, bias=None
  ):
      """
      Layer configuration for MLP generator/discriminator
  
      Arguments:
          name (string): Layer name
          nout (int): Number of output feature maps
          activation (object): Activation function (defaults to ReLu)
          batch_norm(bool): Enable batch normalization (defaults to False)
      """
      layers = []
      layers.append(
          Linear(nout=nout, init=init_w, bsum=batch_norm, name=name)
      )
      if batch_norm:
          layers.append(BatchNorm(name=name + "_bnorm", **bn_prm))
      if bias is not None:
          layers.append(Bias(init=None, name=name + "_bias"))
      layers.append(
          Activation(transform=activation, name=name + "_rectlin")
      )
      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 3 for calling a global function with example usage: mlp_layer(name, nout, activation, batch_norm, bias) and returns: layers