class_docstr: Applies an atrous convolution operator for filtering neighborhoods of 1D inputs.
  A.k.a dilated convolution or convolution with holes.
  Border mode currently supported for this layer is 'valid'.
  Bias will be included in this layer.
  You can also use AtrousConv1D as an alias of this layer.
  The input of this layer should be 3D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  nb_filter: Number of convolution filters to use.
  filter_length: The extension (spatial or temporal) of each filter.
  init: String representation of the initialization method for the weights of the layer.
        Default is 'glorot_uniform'.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Only 'valid' is supported for now.
  subsample_length: Factor by which to subsample output. Int. Default is 1.
  atrous_rate: Factor for kernel dilation. Also called filter_dilation elsewhere.
               Int. Default is 1.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  bias: Only 'True' is supported for now.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> atrousconv1d = AtrousConvolution1D(8, 3, input_shape=(3, 12))
  creating: createZooKerasAtrousConvolution1D
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code: def __init__(
      self,
      nb_filter,
      filter_length,
      init="glorot_uniform",
      activation=None,
      border_mode="valid",
      subsample_length=1,
      atrous_rate=1,
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      if border_mode != "valid":
          invalidInputError(
              False,
              "For AtrousConvolution1D, "
              "only border_mode='valid' is supported for now",
          )
      if not bias:
          invalidInputError(
              False,
              "For AtrousConvolution1D,"
              " only bias=True is supported for now",
          )
      super(AtrousConvolution1D, self).__init__(
          None,
          nb_filter,
          filter_length,
          init,
          activation,
          subsample_length,
          atrous_rate,
          W_regularizer,
          b_regularizer,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new AtrousConvolution1D object from inherited base classes: ZooKerasLayer with example: obj = AtrousConvolution1D(nb_filter, filter_length, init, activation, border_mode, subsample_length, atrous_rate, W_regularizer, b_regularizer, bias, input_shape)