class_docstr: Base class inherited by all feed-forward network classes.
  
  Args:
      hparams (dict, optional): Hyperparameters. Missing
          hyperparamerter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter sturcture and
          default values.
  
  See :meth:`_build` for the inputs and outputs.
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docstr: Feeds forward inputs through the network layers and returns outputs.
  
  Args:
      inputs: The inputs to the network. The requirements on inputs
          depends on the first layer and subsequent layers in the
          network.
      mode (optional): A tensor taking value in
          :tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
          `TRAIN`, `EVAL`, and `PREDICT`. If `None`,
          :func:`texar.tf.global_mode` is used.
  
  Returns:
      The output of the network.
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code: def _build(self, inputs, mode=None):
      """Feeds forward inputs through the network layers and returns outputs.
  
      Args:
          inputs: The inputs to the network. The requirements on inputs
              depends on the first layer and subsequent layers in the
              network.
          mode (optional): A tensor taking value in
              :tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
              `TRAIN`, `EVAL`, and `PREDICT`. If `None`,
              :func:`texar.tf.global_mode` is used.
  
      Returns:
          The output of the network.
      """
      training = is_train_mode(mode)
  
      prev_outputs = inputs
      for layer_id, layer in enumerate(self._layers):
          if isinstance(layer, tf.layers.Dropout) or isinstance(
              layer, tf.layers.BatchNormalization
          ):
              outputs = layer(prev_outputs, training=training)
          else:
              outputs = layer(prev_outputs)
          self._layer_outputs.append(outputs)
          self._layer_outputs_by_name[
              self._layer_names[layer_id]
          ] = outputs
          prev_outputs = outputs
  
      if not self._built:
          self._add_internal_trainable_variables()
          # Add trainable variables of `self._layers` which may be
          # constructed externally.
          for layer in self._layers:
              self._add_trainable_variable(layer.trainable_variables)
          self._built = True
  
      return outputs
  
imports: import tensorflow
  from texar.tf.module_base import ModuleBase
  from texar.tf.utils import TexarError
  from texar.tf.core.layers import get_layer
  from texar.tf.utils.utils import uniquify_str
  from texar.tf.utils.mode import is_train_mode
label_desc: Use image node_id 3 for calling the FeedForwardNetworkBase obj's underlying member method code with example usage: obj._build(inputs, mode) and returns: outputs