code: def atrous_conv1d(
          prev_layer,
          n_filter=32,
          filter_size=2,
          stride=1,
          dilation=1,
          act=None,
          padding='SAME',
          data_format='NWC',
          W_init=tf.truncated_normal_initializer(stddev=0.02),
          b_init=tf.constant_initializer(value=0.0),
          W_init_args=None,
          b_init_args=None,
          name='atrous_1d',
  ):
      
      return Conv1dLayer(
          prev_layer=prev_layer,
          act=act,
          shape=(filter_size, int(prev_layer.outputs.get_shape()[-1]), n_filter),
          stride=stride,
          padding=padding,
          dilation_rate=dilation,
          data_format=data_format,
          W_init=W_init,
          b_init=b_init,
          W_init_args=W_init_args,
          b_init_args=b_init_args,
          name=name,
      )
docs: Simplified version of :class:`AtrousConv1dLayer`.
  
      Parameters
      ----------
      prev_layer : :class:`Layer`
          Previous layer.
      n_filter : int
          The number of filters.
      filter_size : int
          The filter size.
      stride : tuple of int
          The strides: (height, width).
      dilation : int
          The filter dilation size.
      act : activation function
          The activation function of this layer.
      padding : str
          The padding algorithm type: "SAME" or "VALID".
      data_format : str
          Default is 'NWC' as it is a 1D CNN.
      W_init : initializer
          The initializer for the weight matrix.
      b_init : initializer or None
          The initializer for the bias vector. If None, skip biases.
      W_init_args : dictionary
          The arguments for the weight matrix initializer.
      b_init_args : dictionary
          The arguments for the bias vector initializer.
      name : str
          A unique layer name.
  
      Returns
      -------
      :class:`Layer`
          A :class:`AtrousConv1dLayer` object