code: def test_create_sample_weights_output_using_series_weights_and_weight_func_different_series_lengths():
      """
      Test `sample_weights` creation using `series_weights` and `weight_func`
      with series of different lengths.
      """
      new_series = series.copy()
      new_series["series_2"].iloc[:3] = np.nan
      new_X_train = X_train.drop([7, 8, 9]).reset_index(drop=True)
      new_y_train_index = pd.DatetimeIndex(
          [
              "2022-01-07",
              "2022-01-08",
              "2022-01-09",
              "2022-01-10",
              "2022-01-11",
              "2022-01-12",
              "2022-01-13",
              "2022-01-10",
              "2022-01-11",
              "2022-01-12",
              "2022-01-13",
          ],
          dtype="datetime64[ns]",
          freq=None,
      )
  
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          series_weights={"series_1": 1.0, "series_2": 2.0},
          weight_func=custom_weights,
      )
      results = forecaster.create_sample_weights(
          series=new_series,
          X_train=new_X_train,
          y_train_index=new_y_train_index,
      )
  
      expected = np.array(
          [1, 0, 0, 0, 1, 1, 1, 0, 2, 2, 2], dtype=float
      )
  
      assert np.array_equal(results, expected)
  
file_path: skforecast/skforecast/ForecasterAutoregMultiSeriesCustom/tests/test_create_sample_weights.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 11 for calling a global function with example usage: test_create_sample_weights_output_using_series_weights_and_weight_func_different_series_lengths() without return types