code: def __init__(self):
      super().__init__()
      self.input_embedders_parameters = {
          "observation": InputEmbedderParameters(
              activation_function="leaky_relu"
          ),
          "measurements": InputEmbedderParameters(
              activation_function="leaky_relu"
          ),
          "goal": InputEmbedderParameters(
              activation_function="leaky_relu"
          ),
      }
  
      self.input_embedders_parameters["observation"].scheme = [
          Conv2d(32, 8, 4),
          Conv2d(64, 4, 2),
          Conv2d(64, 3, 1),
          Dense(512),
      ]
  
      self.input_embedders_parameters["measurements"].scheme = [
          Dense(128),
          Dense(128),
          Dense(128),
      ]
  
      self.input_embedders_parameters["goal"].scheme = [
          Dense(128),
          Dense(128),
          Dense(128),
      ]
  
      self.middleware_parameters = FCMiddlewareParameters(
          activation_function="leaky_relu",
          scheme=MiddlewareScheme.Empty,
      )
      self.heads_parameters = [
          MeasurementsPredictionHeadParameters(
              activation_function="leaky_relu"
          )
      ]
      self.async_training = False
      self.batch_size = 64
      self.adam_optimizer_beta1 = 0.95
  
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 1 to create a new DFPNetworkParameters object from inherited base classes: NetworkParameters with example: obj = DFPNetworkParameters()