class_docstr: Applies Zoneout on base cell. The implementation follows [KMK16]_.
  
  Compared to mx.gluon.rnn.ZoneoutCell, this implementation uses the same
  mask for output and states[0], since for RNN cells, states[0] is the same
  as output, except for ResidualCell, where states[0] = input + ouptut
  
  Parameters
  ----------
  base_cell
      The cell on which to perform variational dropout.
  zoneout_outputs
      The dropout rate for outputs. Won't apply dropout if it equals 0.
  zoneout_states
      The dropout rate for state inputs on the first state channel.
      Won't apply dropout if it equals 0.
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code: def __repr__(self):
      s = (
          "{name}(p_out={zoneout_outputs}, p_state={zoneout_states},"
          " {base_cell})"
      )
      return s.format(name=self.__class__.__name__, **self.__dict__)
  
imports: from typing import Tuple
  from mxnet.gluon.rnn import BidirectionalCell, ModifierCell, RecurrentCell, SequentialRNNCell
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the RNNZoneoutCell obj's underlying member method code with example usage: obj.__repr__() and returns: s