class_docstr: Simple Conv-1D network which consists of a sequence of conv layers
  followed with a sequence of dense layers.
  
  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. The inputs must be a
  3D Tensor of shape `[batch_size, length, channels]` (default), or
  `[batch_size, channels, length]` (if `data_format` is set to
  `'channels_last'` through :attr:`hparams`). For example, for sequence
  classification, `length` corresponds to time steps, and `channels`
  corresponds to embedding dim.
  
  Example:
  
      .. code-block:: python
  
          nn = Conv1DNetwork() # Use the default structure
  
          inputs = tf.random_uniform([64, 20, 256])
          outputs = nn(inputs)
          # outputs == Tensor of shape [64, 128], cuz the final dense layer
          # has size 128.
  
  .. document private functions
  .. automethod:: _build
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code: def __init__(self, hparams=None):
      FeedForwardNetworkBase.__init__(self, hparams)
  
      with tf.variable_scope(self.variable_scope):
          layer_hparams = self._build_layer_hparams()
          _build_layers(self, layers=None, layer_hparams=layer_hparams)
  
file_path: texar/texar/tf/modules/networks/conv_networks.py
imports: import tensorflow
  from texar.tf.modules.networks.network_base import FeedForwardNetworkBase
  from texar.tf.modules.networks.network_base import _build_layers
  from texar.tf.core.layers import get_pooling_layer_hparams, get_activation_fn
  from texar.tf.utils.utils import uniquify_str
  from texar.tf.utils.shapes import mask_sequences
  from texar.tf.hyperparams import HParams
label_desc: Use image node_id 1 to create a new Conv1DNetwork object from inherited base classes: FeedForwardNetworkBase with example: obj = Conv1DNetwork(hparams)