content: from gae.initializations import *
  import tensorflow as tf
  
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  
  # global unique layer ID dictionary for layer name assignment
  _LAYER_UIDS = {}
  
  
  def get_layer_uid(layer_name=''):
      """Helper function, assigns unique layer IDs
      """
      if layer_name not in _LAYER_UIDS:
          _LAYER_UIDS[layer_name] = 1
          return 1
      else:
          _LAYER_UIDS[layer_name] += 1
          return _LAYER_UIDS[layer_name]
  
  
  def dropout_sparse(x, keep_prob, num_nonzero_elems):
      """Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements)
      """
      noise_shape = [num_nonzero_elems]
      random_tensor = keep_prob
      random_tensor += tf.random_uniform(noise_shape)
      dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
      pre_out = tf.sparse_retain(x, dropout_mask)
      return pre_out * (1./keep_prob)
  
  
  class Layer(object):
      """Base layer class. Defines basic API for all layer objects.
  
      # Properties
          name: String, defines the variable scope of the layer.
  
      # Methods
          _call(inputs): Defines computation graph of layer
              (i.e. takes input, returns output)
          __call__(inputs): Wrapper for _call()
      """
      def __init__(self, **kwargs):
          allowed_kwargs = {'name', 'logging'}
          for kwarg in kwargs.keys():
              assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
          name = kwargs.get('name')
          if not name:
              layer = self.__class__.__name__.lower()
              name = layer + '_' + str(get_layer_uid(layer))
          self.name = name
          self.vars = {}
          logging = kwargs.get('logging', False)
          self.logging = logging
          self.issparse = False
  
      def _call(self, inputs):
          return inputs
  
      def __call__(self, inputs):
          with tf.name_scope(self.name):
              outputs = self._call(inputs)
              return outputs
  
  
  class GraphConvolution(Layer):
      """Basic graph convolution layer for undirected graph without edge labels."""
      def __init__(self, input_dim, output_dim, adj, dropout=0., act=tf.nn.relu, **kwargs):
          super(GraphConvolution, self).__init__(**kwargs)
          with tf.variable_scope(self.name + '_vars'):
              self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights")
          self.dropout = dropout
          self.adj = adj
          self.act = act
  
      def _call(self, inputs):
          x = inputs
          x = tf.nn.dropout(x, 1-self.dropout)
          x = tf.matmul(x, self.vars['weights'])
          x = tf.sparse_tensor_dense_matmul(self.adj, x)
          outputs = self.act(x)
          return outputs
  
  
  class GraphConvolutionSparse(Layer):
      """Graph convolution layer for sparse inputs."""
      def __init__(self, input_dim, output_dim, adj, features_nonzero, dropout=0., act=tf.nn.relu, **kwargs):
          super(GraphConvolutionSparse, self).__init__(**kwargs)
          with tf.variable_scope(self.name + '_vars'):
              self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights")
          self.dropout = dropout
          self.adj = adj
          self.act = act
          self.issparse = True
          self.features_nonzero = features_nonzero
  
      def _call(self, inputs):
          x = inputs
          x = dropout_sparse(x, 1-self.dropout, self.features_nonzero)
          x = tf.sparse_tensor_dense_matmul(x, self.vars['weights'])
          x = tf.sparse_tensor_dense_matmul(self.adj, x)
          outputs = self.act(x)
          return outputs
  
  
  class InnerProductDecoder(Layer):
      """Decoder model layer for link prediction."""
      def __init__(self, input_dim, dropout=0., act=tf.nn.sigmoid, **kwargs):
          super(InnerProductDecoder, self).__init__(**kwargs)
          self.dropout = dropout
          self.act = act
  
      def _call(self, inputs):
          inputs = tf.nn.dropout(inputs, 1-self.dropout)
          x = tf.transpose(inputs)
          x = tf.matmul(inputs, x)
          x = tf.reshape(x, [-1])
          outputs = self.act(x)
          return outputs
  