class_docstr: Applies convolution operator for filtering neighborhoods of 1D inputs.
  You can also use Conv1D 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'.
  limits: Optional. Limit value for initialization method.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Either 'valid' or 'same'. Default is 'valid'.
  subsample_length: Factor by which to subsample output. 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: Whether to include a bias (i.e. make the layer affine rather than linear).
        Default is True.
  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.
  
  >>> conv1d = Convolution1D(12, 4, input_shape=(3, 16))
  creating: createZooKerasConvolution1D
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code: def __init__(
      self,
      nb_filter,
      filter_length,
      init="glorot_uniform",
      limits=None,
      activation=None,
      border_mode="valid",
      subsample_length=1,
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      super(Convolution1D, self).__init__(
          None,
          nb_filter,
          filter_length,
          init,
          list(limits) if limits else None,
          activation,
          border_mode,
          subsample_length,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new Convolution1D object from inherited base classes: ZooKerasLayer with example: obj = Convolution1D(nb_filter, filter_length, init, limits, activation, border_mode, subsample_length, W_regularizer, b_regularizer, bias, input_shape)