docstr: Tests that `Scaler` correctly handles situation where `global_fit = True`. More
  specifically, test checks that global fitting with two disjoint series
  produces same fitted parameters as local fitting with a single series formed
  by 'gluing' these two disjoint series together.
code: def test_global_fitting(self):
      """
      Tests that `Scaler` correctly handles situation where `global_fit = True`. More
      specifically, test checks that global fitting with two disjoint series
      produces same fitted parameters as local fitting with a single series formed
      by 'gluing' these two disjoint series together.
      """
      sine_series = sine_timeseries(
          length=50, value_y_offset=5, value_frequency=0.05
      )
      lin_series = linear_timeseries(
          start_value=1, end_value=10, length=50
      )
      series_combined = sine_series.append_values(
          lin_series.all_values()
      )
      local_fitted_scaler = (
          Scaler(global_fit=False)
          .fit(series_combined)
          ._fitted_params[0]
      )
      global_fitted_scaler = (
          Scaler(global_fit=True)
          .fit([sine_series, lin_series])
          ._fitted_params[0]
      )
      assert (
          local_fitted_scaler.get_params()
          == global_fitted_scaler.get_params()
      )
  
file_path: darts/darts/tests/dataprocessing/transformers/test_data_transformer.py
imports: import numpy
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from darts import TimeSeries
  from darts.dataprocessing.transformers import Scaler
  from darts.utils import timeseries_generation
  from darts.utils.timeseries_generation import linear_timeseries, sine_timeseries
label_desc: Use image node_id 5 for calling the TestDataTransformer obj's underlying member method code with example usage: obj.test_global_fitting() without return types