code: def _update_measurements_targets(self, episode, num_steps):
      if (
          "measurements" not in episode.transitions[0].state
          or episode.transitions[0].state["measurements"] == []
      ):
          raise ValueError(
              "Measurements are not present in the transitions of the last episode played. "
          )
      measurements_size = self.spaces.state["measurements"].shape[0]
      for transition_idx, transition in enumerate(episode.transitions):
          transition.info["future_measurements"] = np.zeros(
              (num_steps, measurements_size)
          )
          for step in range(num_steps):
              offset_idx = transition_idx + 2**step
  
              if offset_idx >= episode.length():
                  if (
                      self.ap.algorithm.handling_targets_after_episode_end
                      == HandlingTargetsAfterEpisodeEnd.NAN
                  ):
                      # the special MSE loss will ignore those entries so that the gradient will be 0 for these
                      transition.info["future_measurements"][
                          step
                      ] = np.nan
                      continue
  
                  elif (
                      self.ap.algorithm.handling_targets_after_episode_end
                      == HandlingTargetsAfterEpisodeEnd.LastStep
                  ):
                      offset_idx = -1
  
              transition.info["future_measurements"][
                  step
              ] = self.target_measurements_scale_factors * (
                  episode.transitions[offset_idx].state["measurements"]
                  - transition.state["measurements"]
              )
  
imports: import copy
  from enum import Enum
  from typing import Union
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.memory import MemoryGranularity
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace
label_desc: Use image node_id 7 for calling the DFPAgent obj's underlying member method code with example usage: obj._update_measurements_targets(episode, num_steps) without return types