class_docstr: Applies Variational Zoneout on base cell. The implementation follows.
  
  [GG16]_. Variational zoneout uses the same mask across time-steps. It can
  be applied to RNN outputs, and states. The masks for them are not shared.
  
  The mask is initialized when stepping forward for the first time and
  willremain the same until .reset() is called. Thus, if using the cell and
  stepping manually without calling .unroll(), the .reset() should be called
  after each sequence.
  
  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 _alias(self):
      return "variationalzoneout"
  
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 3 for calling the VariationalZoneoutCell obj's underlying member method code with example usage: obj._alias() and returns: str