class_docstr: Generalized linear models implementation 
code: def predict(self, input_data):
      input_data = copy(input_data)
      parameters = input_data.task.task_params
      forecast_length = parameters.forecast_length
      old_idx = input_data.idx
      if forecast_length == 1:
          predictions = self.model.predict(
              np.concatenate(
                  [np.array([1]), input_data.idx.astype("float64")]
              ).reshape(-1, 2)
          )
      else:
          predictions = self.model.predict(
              sm.add_constant(input_data.idx.astype("float64")).reshape(
                  -1, 2
              )
          )
  
      start_id = old_idx[-1] - forecast_length + 1
      end_id = old_idx[-1]
      predict = predictions
      predict = np.array(predict).reshape(1, -1)
      new_idx = np.arange(start_id, end_id + 1)
  
      input_data.idx = new_idx
  
      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 5 for calling the GLMImplementation obj's underlying member method code with example usage: obj.predict(input_data) and returns: output_data