class_docstr: Long Short Term Memory unit architecture.
  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'.
  inner_activation: String representation of the activation function for inner cells.
                    Default is 'hard_sigmoid'.
  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.
  
  >>> lstm = LSTM(32, input_shape=(8, 16), name="lstm1")
  creating: createZooKerasLSTM
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code: def __init__(
      self,
      output_dim,
      activation="tanh",
      inner_activation="hard_sigmoid",
      return_sequences=False,
      go_backwards=False,
      W_regularizer=None,
      U_regularizer=None,
      b_regularizer=None,
      input_shape=None,
      **kwargs,
  ):
      super(LSTM, self).__init__(
          None,
          output_dim,
          activation,
          inner_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 LSTM object from inherited base classes: ZooKerasLayer with example: obj = LSTM(output_dim, activation, inner_activation, return_sequences, go_backwards, W_regularizer, U_regularizer, b_regularizer, input_shape)