code: def set_environment_parameters(self, spaces: SpacesDefinition):
      self.spaces = copy.deepcopy(spaces)
      self.spaces.goal = VectorObservationSpace(
          shape=self.spaces.state["measurements"].shape,
          measurements_names=self.spaces.state[
              "measurements"
          ].measurements_names,
      )
  
      # if the user has filled some scale values, check that he got the names right
      if set(
          self.spaces.state["measurements"].measurements_names
      ).intersection(
          self.ap.algorithm.scale_measurements_targets.keys()
      ) != set(
          self.ap.algorithm.scale_measurements_targets.keys()
      ):
          raise ValueError(
              "Some of the keys in parameter scale_measurements_targets ({})  are not defined in "
              "the measurements space {}".format(
                  self.ap.algorithm.scale_measurements_targets.keys(),
                  self.spaces.state["measurements"].measurements_names,
              )
          )
  
      super().set_environment_parameters(self.spaces)
  
      # the below is done after calling the base class method, as it might add accumulated reward as a measurement
  
      # fill out the missing measurements scale factors
      for measurement_name in self.spaces.state[
          "measurements"
      ].measurements_names:
          if (
              measurement_name
              not in self.ap.algorithm.scale_measurements_targets
          ):
              self.ap.algorithm.scale_measurements_targets[
                  measurement_name
              ] = 1
  
      self.target_measurements_scale_factors = np.array(
          [
              self.ap.algorithm.scale_measurements_targets[
                  measurement_name
              ]
              for measurement_name in self.spaces.state[
                  "measurements"
              ].measurements_names
          ]
      )
  
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 5 for calling the DFPAgent obj's underlying member method code with example usage: obj.set_environment_parameters(spaces) without return types