class_docstr: Implementation of recurrent layers.
class_docstr_tok: ["Implementation","of","recurrent","layers","."]
code: def __call__(
      self, inputs, states=None, sequence_length=None, **kwargs
  ):
      self.skip_states = states is None
      if states is None:
          batch_size = inputs.shape[self._layout.find("N")]
          states = self.begin_state(
              batch_size, device=inputs.device, dtype=inputs.dtype
          )
      if isinstance(states, tensor_types):
          states = [states]
  
      if self._use_sequence_length:
          return super(_RNNLayer, self).__call__(
              inputs, states, sequence_length, **kwargs
          )
      else:
          return super(_RNNLayer, self).__call__(
              inputs, states, **kwargs
          )
  
imports: from ...None import np, npx, initializer, cpu
  from ..None import HybridBlock, tensor_types
  from ..parameter import Parameter
  from ...util import use_np
label_desc: Use image node_id 6 for calling the _RNNLayer obj's underlying member method code with example usage: obj.__call__(inputs, states, sequence_length) and returns: super, super