code: def __init__(self, inputs, model):
      # Store our output shape.
      custom_model_config = model.model_config["custom_model_config"]
      if "output_dim" in custom_model_config:
          self.output_shape = tf.concat(
              [tf.shape(inputs)[:1], custom_model_config["output_dim"]],
              axis=0,
          )
      else:
          self.output_shape = tf.shape(inputs)
      super().__init__(inputs, model)
  
imports: from functools import partial
  from gymnasium.spaces import Box, Dict, Discrete, Tuple
  import numpy
  import unittest
  import ray
  from ray.rllib.models import ActionDistribution, ModelCatalog, MODEL_DEFAULTS
  from ray.rllib.models.preprocessors import Preprocessor, TupleFlatteningPreprocessor
  from ray.rllib.models.tf.tf_action_dist import MultiActionDistribution, TFActionDistribution
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 1 to create a new CustomActionDistribution object from inherited base classes: TFActionDistribution with example: obj = CustomActionDistribution(inputs, model)