step = function step(inputs, states) {
	        // `inputs` and `states` are concatenated to form a single `Array` of
	        // `tf.Tensor`s as the input to `cell.call()`.
	        var outputs = _this4.cell.call([inputs].concat(states), cellCallKwargs); // Marshall the return value into output and new states.


	        return [outputs[0], outputs.slice(1)];
	      }
Reset the state tensors of the RNN.

If the `states` argument is `undefined` or `null`, will set the
state tensor(s) of the RNN to all-zero tensors of the appropriate
shape(s).

If `states` is provided, will set the state tensors of the RNN to its
value.

@param states Optional externally-provided initial states.
@param training Whether this call is done during training. For stateful
  RNNs, this affects whether the old states are kept or discarded. In
  particular, if `training` is `true`, the old states will be kept so
  that subsequent backpropgataion through time (BPTT) may work properly.
  Else, the old states will be discarded.