code: def make_model_and_action_dist(
      policy, observation_space, action_space, config
  ):
      # Policy network.
      dist_class, dist_dim = ModelCatalog.get_action_dist(
          action_space,
          config["model"],  # model_options
          dist_type="deterministic",
          framework="torch",
      )
      model = ModelCatalog.get_model_v2(
          policy.preprocessor.observation_space,
          action_space,
          num_outputs=dist_dim,
          model_config=config["model"],
          framework="torch",
      )
      # Make all model params not require any gradients.
      for p in model.parameters():
          p.requires_grad = False
      return model, dist_class
  
file_path: ray/rllib_contrib/es/src/rllib_es/es/es_torch_policy.py
imports: import gymnasium
  import numpy
  import tree
  import ray
  from ray.rllib.models import ModelCatalog
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.filter import get_filter
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space, unbatch
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 3 for calling a global function with example usage: make_model_and_action_dist(policy, observation_space, action_space, config) and returns: model,  dist_class