class_docstr: A fully-connected recurrent neural network cell. The output is to be fed back to input.
  The input of this layer should be 3D, i.e. (batch, time steps, 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
  output_dim: Hidden unit size. Dimension of internal projections and final output.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is 'tanh'.
  return_sequences: Whether to return the full sequence or only return the last output
                    in the output sequence. Default is False.
  go_backwards: Whether the input sequence will be processed backwards. Default is False.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  U_regularizer: An instance of [[Regularizer]], applied the recurrent weights matrices.
                 Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  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.
  
  >>> simplernn = SimpleRNN(16, input_shape=(3, 32))
  creating: createZooKerasSimpleRNN
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code: def __init__(
      self,
      output_dim,
      activation="tanh",
      return_sequences=False,
      go_backwards=False,
      W_regularizer=None,
      U_regularizer=None,
      b_regularizer=None,
      input_shape=None,
      **kwargs,
  ):
      super(SimpleRNN, self).__init__(
          None,
          output_dim,
          activation,
          return_sequences,
          go_backwards,
          W_regularizer,
          U_regularizer,
          b_regularizer,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: BigDL/python/dllib/src/bigdl/dllib/keras/layers/recurrent.py
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new SimpleRNN object from inherited base classes: ZooKerasLayer with example: obj = SimpleRNN(output_dim, activation, return_sequences, go_backwards, W_regularizer, U_regularizer, b_regularizer, input_shape)