code: def test_dlt_predict_seed(make_weekly_data, estimator, random_seed):
      train_df, test_df, coef = make_weekly_data
      args = {
          "response_col": "response",
          "date_col": "week",
          "seasonality": 52,
          "n_bootstrap_draws": 100,
          "verbose": False,
          "estimator": estimator,
      }
  
      if estimator == "stan-mcmc":
          args.update({"num_warmup": 50, "num_sample": 100})
      elif estimator == "pyro-svi":
          args.update({"num_steps": 10})
  
      dlt = DLT(**args)
      dlt.fit(train_df)
      predict_df1 = dlt.predict(test_df, seed=random_seed)
      predict_df2 = dlt.predict(test_df, seed=random_seed)
  
      assert all(
          predict_df1["prediction"].values
          == predict_df2["prediction"].values
      )
  
imports: from copy import copy
  import pytest
  import numpy
  from orbit.models import DLT
  from orbit.constants.constants import PredictionKeys
  from orbit.exceptions import ModelException, PredictionException
label_desc: Use image node_id 20 for calling a global function with example usage: test_dlt_predict_seed(make_weekly_data, estimator, random_seed) without return types