class_docstr: Custom model for DQN.
class_docstr_tok: ["Custom","model","for","DQN","."]
code: def __init__(
      self,
      obs_space,
      action_space,
      num_outputs,
      model_config,
      name,
      **kw,
  ):
      super(MyKerasQModel, self).__init__(
          obs_space, action_space, num_outputs, model_config, name, **kw
      )
  
      # Define the core model layers which will be used by the other
      # output heads of DistributionalQModel
      self.inputs = tf.keras.layers.Input(
          shape=obs_space.shape, name="observations"
      )
      layer_1 = tf.keras.layers.Dense(
          128,
          name="my_layer1",
          activation=tf.nn.relu,
          kernel_initializer=normc_initializer(1.0),
      )(self.inputs)
      layer_out = tf.keras.layers.Dense(
          num_outputs,
          name="my_out",
          activation=tf.nn.relu,
          kernel_initializer=normc_initializer(1.0),
      )(layer_1)
      self.base_model = tf.keras.Model(self.inputs, layer_out)
  
imports: import argparse
  import os
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.dqn.distributional_q_tf_model import DistributionalQTFModel
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.tf.misc import normc_initializer
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.tf.visionnet import VisionNetwork
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY
  from ray.tune.registry import get_trainable_cls
label_desc: Use image node_id 1 to create a new MyKerasQModel object from inherited base classes: DistributionalQTFModel with example: obj = MyKerasQModel(obs_space, action_space, num_outputs, model_config, name)