def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,

               state_keep_prob=1.0, variational_recurrent=False,

               input_size=None, dtype=None, seed=None):

Args:

      cell: an RNNCell, a projection to output_size is added to it.

      input_keep_prob: unit Tensor or float between 0 and 1, input keep

        probability; if it is constant and 1, no input dropout will be added.

      output_keep_prob: unit Tensor or float between 0 and 1, output keep

        probability; if it is constant and 1, no output dropout will be added.

      state_keep_prob: unit Tensor or float between 0 and 1, output keep

        probability; if it is constant and 1, no output dropout will be added.

        State dropout is performed on the *output* states of the cell.

      variational_recurrent: Python bool.  If `True`, then the same

        dropout pattern is applied across all time steps per run call.

        If this parameter is set, `input_size` **must** be provided.

      input_size: (optional) (possibly nested tuple of) `TensorShape` objects

        containing the depth(s) of the input tensors expected to be passed in to

        the `DropoutWrapper`.  Required and used **iff**

         `variational_recurrent = True` and `input_keep_prob < 1`.

      dtype: (optional) The `dtype` of the input, state, and output tensors.

        Required and used **iff** `variational_recurrent = True`.

      seed: (optional) integer, the randomness seed.