from tensorflow.contrib import layers

        nn_activations = layers.fully_connected(nn_activations,
                                                self.params.layer_size)
      return nn_activations


class ManyToOneLayer(hybrid_layer.HybridLayer):

  def _define_vars(self, params):
    pass

  def inference_graph(self, data):
    with ops.device(self.device_assigner):
      # Compute activations for the neural network.
      nn_activations = layers.fully_connected(data, 1)

      # There is always one activation per instance by definition, so squeeze
      # away the extra dimension.
      return array_ops.squeeze(nn_activations, squeeze_dims=[1])


class FlattenedFullyConnectedLayer(hybrid_layer.HybridLayer):
  """A stacked, fully-connected flattened feed-forward neural network layer."""

  def _define_vars(self, params):
    pass

