class_docstr: Densely connected highway network. Highway layers are a natural extension of LSTMs
  to feedforward networks.
  The input of this layer should be 2D, i.e. (batch, input dim).
  
  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
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  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_dim: Dimensionality of the input. Alternatively, you can specify 'input_shape'
             when using this layer as the first layer.
  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.
  
  >>> highway = Highway(activation='relu', input_shape=(8, ))
  creating: createZooKerasHighway
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code: def __init__(
      self,
      activation=None,
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_dim=None,
      input_shape=None,
      **kwargs,
  ):
      if input_dim:
          input_shape = (input_dim,)
      super(Highway, self).__init__(
          None,
          activation,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from bigdl.dllib.utils.common import INTMIN
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new Highway object from inherited base classes: ZooKerasLayer with example: obj = Highway(activation, W_regularizer, b_regularizer, bias, input_dim, input_shape)