Type annotations (#264)

This is a port of the work by: Krzysztof Choromanski, Mark Rowland,
Vikas Sindhwani, Richard E. Turner, Adrian Weller:  "Structured Evolution
with Compact Architectures for Scalable Policy Optimization",
https://arxiv.org/abs/1804.02395

Author: salaast

compiler_opt/es/gradient_ascent_optimization_algorithms.py

@@ -59,7 +59,7 @@ def run_step(self, current_input: npt.NDArray[np.float32],
     raise NotImplementedError("Abstract method")
 
   @abc.abstractmethod
-  def get_state(self) -> List[np.float32]:
+  def get_state(self) -> List[float]:
     """Returns the state of the optimizer.
 
     Returns the state of the optimizer.
@@ -116,7 +116,7 @@ def run_step(self, current_input: npt.NDArray[np.float32],
 
     return current_input + step
 
-  def get_state(self) -> List[np.float32]:
+  def get_state(self) -> List[float]:
     return self.moving_average.tolist()
 
   def set_state(self, state: npt.NDArray[np.float32]) -> None: