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: Return a tensor or tuple of tensors for an initial rnn state.
docstr_tok: ["Return","a","tensor","or","tuple","of","tensors","for","an","initial","rnn","state","."]
code: def _get_rnn_zero_state(self, batch_size):
      """Return a tensor or tuple of tensors for an initial rnn state."""
      return self._rnn_cell.zero_state(batch_size, tf.float32)
  
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 2 for calling the RnnNade obj's underlying member method code with example usage: obj._get_rnn_zero_state(batch_size) and returns: self