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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docstr: Feeds forward inputs through the network layers and returns outputs.
  
  Args:
      inputs: The inputs to the network, which is a 3D tensor.
      sequence_length (optional): An int tensor of shape `[batch_size]`
          containing the length of each element in :attr:`inputs`.
          If given, time steps beyond the length will first be masked out
          before feeding to the layers.
      dtype (optional): Type of the inputs. If not provided, infers
          from inputs automatically.
      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 final layer.
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code: def _build(
      self,  # pylint: disable=arguments-differ
      inputs,
      sequence_length=None,
      dtype=None,
      mode=None,
  ):
      """Feeds forward inputs through the network layers and returns outputs.
  
      Args:
          inputs: The inputs to the network, which is a 3D tensor.
          sequence_length (optional): An int tensor of shape `[batch_size]`
              containing the length of each element in :attr:`inputs`.
              If given, time steps beyond the length will first be masked out
              before feeding to the layers.
          dtype (optional): Type of the inputs. If not provided, infers
              from inputs automatically.
          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 final layer.
      """
      if sequence_length is not None:
          inputs = mask_sequences(
              inputs,
              sequence_length,
              dtype=dtype,
              time_major=False,
              tensor_rank=3,
          )
      return super(Conv1DNetwork, self)._build(inputs, mode=mode)
  
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 7 for calling the Conv1DNetwork obj's underlying member method code with example usage: obj._build(inputs, sequence_length, dtype, mode) and returns: super