class_docstr: Wraps a Keras RNN/LSTM/GRU layer to make network state more consistent.
code: def get_initial_state(self, inputs=None):
      inputs_flat = [
          tf.convert_to_tensor(x, name="input", dtype_hint=self.dtype)
          for x in tf.nest.flatten(inputs)
      ]
      has_time_axis = all(
          [
              x.shape.ndims is None or x.shape.ndims > 2
              for x in inputs_flat
          ]
      )
      if not has_time_axis:
          inputs_flat = [tf.expand_dims(t, axis=1) for t in inputs_flat]
      inputs = tf.nest.pack_sequence_as(inputs, inputs_flat)
      return self._layer.get_initial_state(inputs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import common
label_desc: Use image node_id 14 for calling the RNNWrapper obj's underlying member method code with example usage: obj.get_initial_state(inputs) and returns: self