code: def test_lgt_is_fitted(
      iclaims_training_data, estimator, keep_samples, point_method
  ):
      df = iclaims_training_data
      df["claims"] = np.log(df["claims"])
      regressor_col = ["trend.unemploy"]
  
      if estimator == "stan-mcmc":
          lgt = LGT(
              response_col="claims",
              date_col="week",
              regressor_col=regressor_col,
              seasonality=52,
              seed=8888,
              num_warmup=50,
              num_sample=50,
              verbose=False,
              estimator=estimator,
          )
      elif estimator == "pyro-svi":
          lgt = LGT(
              response_col="claims",
              date_col="week",
              regressor_col=regressor_col,
              seasonality=52,
              seed=8888,
              num_steps=10,
              verbose=False,
              estimator=estimator,
          )
      lgt.fit(df, keep_samples=keep_samples, point_method=point_method)
      is_fitted = lgt.is_fitted()
  
      # still True when keep_samples is False
      assert is_fitted
  
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 14 for calling a global function with example usage: test_lgt_is_fitted(iclaims_training_data, estimator, keep_samples, point_method) without return types