code: def test_glm_indexes_correct():
      """
      Test  checks correct indexing after performing non-lagged operation.
      For example we generate time series [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
      and use GLM as estimator
  
      output should looks like
      [
      [0+N(0, 0.1), 1+N(0, 0.1)],
      [1+N(0, 0.1), 2+N(0, 0.1)],
      [2+N(0, 0.1), 3+N(0, 0.1)],
      [3+N(0, 0.1), 4+N(0, 0.1)],
      [4+N(0, 0.1), 5+N(0, 0.1)],
      [5+N(0, 0.1), 6+N(0, 0.1)],
      [6+N(0, 0.1), 7+N(0, 0.1)],
      [7+N(0, 0.1), 8+N(0, 0.1)],
      [8+N(0, 0.1 9+N(0, 0.1)]
      ]
  
      and indexes look like [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
      """
      input_data = generate_simple_series()
      glm_impl = GLMImplementation(
          OperationParameters(
              **{"family": "gaussian", "link": "identity"}
          )
      )
      glm_impl.fit(input_data)
      predicted = glm_impl.predict_for_fit(input_data)
      pred_values = predicted.predict
      for i in range(9):
          assert pred_values[i, 0] - i < 0.5
          assert predicted.idx[i] - pred_values[i, 0] < 0.5
  
imports: import pickle
  from copy import deepcopy
  from time import perf_counter
  from typing import Tuple, Optional
  import numpy
  import pytest
  from golem.core.log import default_log
  from sklearn.datasets import make_classification
  from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
  from sklearn.preprocessing import MinMaxScaler
  from fedot.core.constants import FAST_TRAIN_PRESET_NAME
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import PCAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.discriminant_analysis import LDAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.naive import NaiveAverageForecastImplementation, RepeatLastValueImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import GLMImplementation
  from fedot.core.operations.model import Model
  from fedot.core.operations.operation_parameters import get_default_params, OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.operation_types_repository import OperationMetaInfo, OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.data_operations.test_time_series_operations import synthetic_univariate_ts
  from test.unit.tasks.test_forecasting import get_ts_data, get_ts_data_with_dt_idx
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 18 for calling a global function with example usage: test_glm_indexes_correct() without return types