code: def get_mm_opt(self, datatype, port, mm_prefix="", mm_suffix=""):
      model_model_opt = {}
      model_model_opt["exact_api_call"] = True
      model_model_opt["api_schemas"] = self.opt["api_schemas"]
      model_model_opt[
          "display_examples"
      ] = False  # we'll see these later
      model_model_opt["episodes_randomization_seed"] = 42
      if "nucleus" in mm_prefix or "nucleus" in mm_suffix:
          model_model_opt[
              "episodes_randomization_seed"
          ] = random.randint(0, 1000000000000)
      model_model_opt["skip_generation"] = False
      model_model_opt["batchsize"] = 32
      model_model_opt["num_episodes"] = -1
      model_model_opt["datatype"] = datatype
      model_model_opt["log_keep_fields"] = "all"
      # Do this so that the agents get the ride settings
      model_model_opt["override"] = copy.deepcopy(model_model_opt)
  
      if self.opt["api_schemas"]:
          grounding = "parlai.tasks.google_sgd_simulation_splits.agents:OutDomainSingleApiSchemaAgent"
      else:
          grounding = "parlai.core.tod.tod_agents_and_teachers:TodEmptyApiSchemaAgent"
  
      model_model_opt["api_schema_grounding_model"] = grounding
      model_model_opt[
          "goal_grounding_model"
      ] = "parlai.tasks.google_sgd_simulation_splits.agents:OutDomainSingleGoalAgent"
      model_model_opt[
          "api_resp_model"
      ] = "parlai.core.tod.tod_agents_and_teachers:TodStandaloneApiAgent"
      model_model_opt["standalone_api_file"] = STANDALONE_API_FILE_PATH
  
      model_model_opt["system_model_file"] = self.opt["model_file"]
      model_model_opt["user_model_file"] = self.opt["model_file"]
      model_model_opt["distributed_world_size"] = self.opt[
          "distributed_world_size"
      ]
      model_model_opt["ddp_backend"] = self.opt["ddp_backend"]
      model_model_opt["save_format"] = "conversations"
      model_model_opt["log_every_n_seconds"] = 30
  
      if self.opt["custom_model_model_name"] is None:
          model_model_name = ""
          if "zoo:bart" in self.opt["init_model"]:
              model_model_name = "BartOnlyNoApi"
      else:
          model_model_name = self.opt["custom_model_model_name"]
  
      model_model_name += "_ApiSchemas" + str(self.opt["api_schemas"])
      lr = self.opt["learningrate"]
      multitask = "Multitask-" + str(self.opt["multitask_weights"][0])
      model_model_name = f"{model_model_name}_{lr}_{multitask}"
  
      base_path_name = os.path.dirname(self.opt["model_file"])
  
      model_model_opt["report_filename"] = os.path.join(
          base_path_name, mm_prefix + model_model_name + mm_suffix
      )
      model_model_opt["world_logs"] = os.path.join(
          base_path_name, mm_prefix + model_model_name + mm_suffix
      )
      model_model_opt["port"] = port
  
      pretty_print = ""
  
      for key in model_model_opt:
          if key == "override":
              continue
          pretty_print += "--" + key.replace("_", "-")
          pretty_print += " "
          pretty_print += str(model_model_opt[key])
          pretty_print += " "
      self.dist_print(pretty_print)
  
      return model_model_opt
  
imports: from parlai.core.script import ParlaiScript, register_script
  from parlai.scripts.train_model import TrainModel
  from parlai.scripts.distributed_train import DistributedTrain, setup_args
  from parlai.scripts.eval_model import EvalModel
  from parlai.scripts.distributed_eval import DistributedEval
  from parlai.scripts.tod_world_script import TodWorldScript
  from parlai.scripts.distributed_tod_world_script import DistributedTodWorldScript
  from projects.tod_simulator.scripts.get_passing_only import GetPassingOnlyScript
  from projects.tod_simulator.scripts.get_al_samples_for_gsgd import GetAlSamplesForGsgdScript, setup_args
  import copy
  import os
  from shutil import copyfile
  import json
  import random
label_desc: Use image node_id 1 for calling the TodDistributedUberScript obj's underlying member method code with example usage: obj.get_mm_opt(datatype, port, mm_prefix, mm_suffix) and returns: model_model_opt