class_docstr: ### Description
  
  ### Diagram
  
  ### Constraints
class_docstr_tok: ["#","#","#","Description","#","#","#","Diagram","#","#","#","Constraints"]
docstr: :param hidden_layer_channels: array, number of output channels for each layer
  :param name: layer name
docstr_tok: [":","param","hidden_layer_channels",":","array",",","number","of","output","channels","for","each","layer",":","param","name",":","layer","name"]
code: def __init__(self, hidden_layer_channels, name):
      """
  
      :param hidden_layer_channels: array, number of output channels for each layer
      :param name: layer name
      """
      super(ImageDiscriminator, self).__init__(name=name)
      self._layer_channels = hidden_layer_channels
      self.initializers = {
          "w": tf.contrib.layers.variance_scaling_initializer(),
          "b": tf.constant_initializer(0),
      }
  
file_path: NiftyNet/niftynet/network/simple_gan.py
imports: from __future__ import absolute_import, print_function
  import tensorflow
  import numpy
  from niftynet.layer.base_layer import TrainableLayer
label_desc: Use image node_id 1 to create a new ImageDiscriminator object from inherited base classes: TrainableLayer with example: obj = ImageDiscriminator(hidden_layer_channels, name)