code: def conv_layer(
      name,
      n_feature,
      ker_size=4,
      strides=2,
      padding=1,
      activation=lrelu,
      batch_norm=True,
      bias=None,
  ):
      """
      Layer configuration for deep-convolutional (DC) discriminator
  
      Arguments:
          name (string): Layer name
          n_feature (int): Number of output feature maps
          ker_size (int): Size of convolutional kernel (defaults to 4)
          strides (int): Stride of convolution (defaults to 2)
          padding (int): Padding of convolution (defaults to 1)
          activation (object): Activation function (defaults to leaky ReLu)
          batch_norm(bool): Enable batch normalization (defaults to True)
      """
      layers = []
      layers.append(
          Convolution(
              fshape=(ker_size, ker_size, n_feature),
              strides=strides,
              padding=padding,
              dilation={},
              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 1 for calling a global function with example usage: conv_layer(name, n_feature, ker_size, strides, padding, activation, batch_norm, bias) and returns: layers