class_docstr: Generalized linear models implementation 
code: def predict_for_fit(self, input_data: InputData) -> OutputData:
      input_data = copy(input_data)
      parameters = input_data.task.task_params
      forecast_length = parameters.forecast_length
      old_idx = input_data.idx
      target = input_data.target
      predictions = self.model.predict(
          sm.add_constant(input_data.idx.astype("float64")).reshape(
              -1, 2
          )
      )
      _, predict = ts_to_table(
          idx=old_idx,
          time_series=predictions,
          window_size=forecast_length,
      )
      new_idx, target_columns = ts_to_table(
          idx=old_idx, time_series=target, window_size=forecast_length
      )
  
      input_data.idx = new_idx
      input_data.target = target_columns
  
      output_data = self._convert_to_output(
          input_data, predict=predict, data_type=DataTypesEnum.table
      )
      return output_data
  
imports: from copy import copy
  import numpy
  import statsmodels.api
  from statsmodels.genmod.families import Gamma, Gaussian, InverseGaussian
  from statsmodels.genmod.families.links import identity, inverse_power, inverse_squared, log
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ts_to_table
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 6 for calling the GLMImplementation obj's underlying member method code with example usage: obj.predict_for_fit(input_data) and returns: output_data