class_docstr: Custom model for DQN.
class_docstr_tok: ["Custom","model","for","DQN","."]
code: def forward(self, input_dict, state, seq_lens):
      model_out = self.base_model(input_dict["obs"])
      return model_out, state
  
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 2 for calling the MyKerasQModel obj's underlying member method code with example usage: obj.forward(input_dict, state, seq_lens) and returns: model_out,  state