class_docstr: RNN-NADE [2], a NADE parameterized by an RNN.
  
  The NADE's bias parameters are given by the output of the RNN.
  
  [2]: https://arxiv.org/abs/1206.6392
  
  Args:
    rnn_cell: The tf.nn.rnn_cell.RnnCell to use.
    num_dims: The number of binary dimensions for each observation.
    num_hidden: The number of hidden units in the NADE.
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docstr: Create an RnnNadeStateTuple of zeros.
  
  Args:
    batch_size: batch size.
  
  Returns:
    An RnnNadeStateTuple of zeros.
docstr_tok: ["Create","an","RnnNadeStateTuple","of","zeros",".","Args",":","batch_size",":","batch","size",".","Returns",":","An","RnnNadeStateTuple","of","zeros","."]
code: def zero_state(self, batch_size):
      """Create an RnnNadeStateTuple of zeros.
  
      Args:
        batch_size: batch size.
  
      Returns:
        An RnnNadeStateTuple of zeros.
      """
      with tf.name_scope("RnnNadeZeroState", values=[batch_size]):
          zero_state = self._get_rnn_zero_state(batch_size)
          return RnnNadeStateTuple(
              tf.zeros(
                  (batch_size, self._nade.num_hidden), name="b_enc"
              ),
              tf.zeros((batch_size, self._num_dims), name="b_dec"),
              zero_state,
          )
  
file_path: magenta/magenta/models/pianoroll_rnn_nade/pianoroll_rnn_nade_graph.py
imports: import collections
  import magenta
  from magenta.common import Nade
  from magenta.contrib import seq2seq
  from magenta.models.shared import events_rnn_graph
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 7 for calling the RnnNade obj's underlying member method code with example usage: obj.zero_state(batch_size) and returns: RnnNadeStateTuple