code: def test_scaling_multi_series(self):
      # 5 categories in total for each categorical static covariate from multiple time series
      scaler = StaticCovariatesTransformer()
      series_tr2 = scaler.fit_transform([self.series1, self.series2])
  
      np.testing.assert_almost_equal(
          series_tr2[0].static_covariates_values(),
          np.array(
              [
                  [0.0, 0.0, 0.0, 0.0],
                  [0.25, 1.0, 0.25, 1.0],
                  [0.5, 2.0, 0.5, 2.0],
              ]
          ),
      )
      series_recovered2 = scaler.inverse_transform(series_tr2[0])
      assert self.series1.static_covariates.equals(
          series_recovered2.static_covariates
      )
  
      np.testing.assert_almost_equal(
          series_tr2[1].static_covariates_values(),
          np.array(
              [
                  [0.5, 2.0, 0.5, 2.0],
                  [0.75, 3.0, 0.75, 3.0],
                  [1.0, 4.0, 1.0, 4.0],
              ]
          ),
      )
      series_recovered3 = scaler.inverse_transform(series_tr2[1])
      assert self.series2.static_covariates.equals(
          series_recovered3.static_covariates
      )
  
      series_recovered_multi = scaler.inverse_transform(series_tr2)
      assert self.series1.static_covariates.equals(
          series_recovered_multi[0].static_covariates
      )
      assert self.series2.static_covariates.equals(
          series_recovered_multi[1].static_covariates
      )
  
file_path: darts/darts/tests/dataprocessing/transformers/test_static_covariates_transformer.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
  from darts import TimeSeries
  from darts.dataprocessing.transformers import StaticCovariatesTransformer
  from darts.utils import timeseries_generation
label_desc: Use image node_id 5 for calling the TestStaticCovariatesTransformer obj's underlying member method code with example usage: obj.test_scaling_multi_series() without return types