class_docstr: Feed-forward neural network that consists of a sequence of layers.
  
  Args:
      layers (list, optional): A list of :tf_main:`Layer <layers/Layer>`
          instances composing the network. If not given, layers are created
          according to :attr:`hparams`.
      hparams (dict, optional): Embedder hyperparameters. Missing
          hyperparamerter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter sturcture and
          default values.
  
  See :meth:`~texar.tf.modules.RNNDecoderBase._build` of
  :class:`~texar.tf.modules.FeedForwardNetworkBase` for the inputs and outputs.
  
  Example:
  
      .. code-block:: python
  
          hparams = { # Builds a two-layer dense NN
              "layers": [
                  { "type": "Dense", "kwargs": { "units": 256 },
                  { "type": "Dense", "kwargs": { "units": 10 }
              ]
          }
          nn = FeedForwardNetwork(hparams=hparams)
  
          inputs = tf.random_uniform([64, 100])
          outputs = nn(inputs)
          # outputs == Tensor of shape [64, 10]
code: def __init__(self, layers=None, hparams=None):
      FeedForwardNetworkBase.__init__(self, hparams)
  
      with tf.variable_scope(self.variable_scope):
          _build_layers(
              self, layers=layers, layer_hparams=self._hparams.layers
          )
  
imports: import tensorflow
  from texar.tf.modules.networks.network_base import FeedForwardNetworkBase
  from texar.tf.modules.networks.network_base import _build_layers
label_desc: Use image node_id 1 to create a new FeedForwardNetwork object from inherited base classes: FeedForwardNetworkBase with example: obj = FeedForwardNetwork(layers, hparams)