code: def test_lgt_full_fit(
      make_weekly_data,
      seasonality,
      estimator,
      n_bootstrap_draws,
      store_prediction_array,
      point_method,
  ):
      train_df, test_df, coef = make_weekly_data
      args = {
          "response_col": "response",
          "date_col": "week",
          "prediction_percentiles": [5, 95],
          "seasonality": seasonality,
          "n_bootstrap_draws": n_bootstrap_draws,
          "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})
  
      # if seasonality == 52:
      #     expected_num_parameters += 2
  
      lgt = LGT(**args)
      lgt.fit(train_df, point_method=point_method)
      if estimator == "stan-mcmc":
          expected_num_parameters = (
              len(lgt._model.get_model_param_names()) + 1
          )
      elif estimator == "pyro-svi":
          expected_num_parameters = len(
              lgt._model.get_model_param_names()
          )
  
      init_values = lgt._model.get_init_values()
      if seasonality:
          assert init_values["init_sea"].shape == (51,)
      else:
          assert init_values is None
  
      predict_df = lgt.predict(
          test_df, store_prediction_array=store_prediction_array
      )
  
      _ = lgt.get_prediction_meta()
      _ = lgt.get_training_metrics()
  
      if not n_bootstrap_draws and point_method:
          expected_columns = ["week", "prediction"]
          expected_shape = (51, len(expected_columns))
      else:
          expected_columns = [
              "week",
              "prediction_5",
              "prediction",
              "prediction_95",
          ]
          expected_shape = (51, len(expected_columns))
  
      assert predict_df.shape == expected_shape
      assert predict_df.columns.tolist() == expected_columns
      assert len(lgt._posterior_samples) == expected_num_parameters
  
imports: from copy import copy
  import pytest
  import numpy
  from orbit.models import LGT
  from orbit.constants.constants import PredictionKeys
label_desc: Use image node_id 2 for calling a global function with example usage: test_lgt_full_fit(make_weekly_data, seasonality, estimator, n_bootstrap_draws, store_prediction_array, point_method) without return types