code: def add_human_observations_to_replay_memory(self):
      keyboard_key_value_label_mapping = (
          self._generate_keyboard_key_value_mapping()
      )
      input_label_action_space_mapping = dict()
  
      for label_action_space_value in list(
          enumerate(self.game_inputs[0]["inputs"])
      ):
          input_label_action_space_mapping[
              label_action_space_value[1]
          ] = label_action_space_value[0]
  
      with h5py.File(
          f"datasets/{self.name}_input_recording.h5", "r"
      ) as f:
          timestamps = set()
  
          for key in f.keys():
              timestamps.add(float(key.split("-")[0]))
  
          for timestamp in sorted(list(timestamps)):
              # Frames
              png_frames = f[f"{timestamp}-frames"].value
              numpy_frames = [
                  skimage.util.img_as_float(
                      skimage.io.imread(io.BytesIO(b))
                  )
                  for b in png_frames
              ]
              pytorch_frames = [
                  torch.tensor(
                      torch.from_numpy(frame), dtype=torch.float32
                  )
                  for frame in numpy_frames
              ]
              frames = torch.stack(pytorch_frames, 0)
  
              # Action
              action_key = tuple(
                  sorted(
                      [
                          b.decode("utf-8")
                          for b in f[
                              f"{timestamp}-keyboard-inputs-active"
                          ]
                      ]
                  )
              )
  
              if action_key not in keyboard_key_value_label_mapping:
                  continue
  
              label = keyboard_key_value_label_mapping[action_key]
              action = input_label_action_space_mapping[label]
  
              # Reward
              reward = f[f"{timestamp}-reward"].value
  
              # Terminal Flag
              terminal = f[f"{timestamp}-terminal"].value
  
              self.replay_memory.append(
                  frames, action, reward, terminal
              )
              self.remaining_observe_steps -= 1
  
              if self.remaining_observe_steps == 0:
                  self.set_mode(RainbowDQNAgentModes.TRAIN)
                  break
  
imports: from serpent.machine_learning.reinforcement_learning.agent import Agent
  from serpent.game_frame import GameFrame
  from serpent.game_frame_buffer import GameFrameBuffer
  from serpent.input_controller import KeyboardEvent, MouseEvent
  from serpent.enums import InputControlTypes
  from serpent.logger import Loggers
  from serpent.utilities import SerpentError
  import os
  import io
  import enum
  import random
  import json
  import numpy
  import h5py
  import skimage.io
  import skimage.util
label_desc: Use image node_id 5 for calling the RainbowDQNAgent obj's underlying member method code with example usage: obj.add_human_observations_to_replay_memory() without return types