class_docstr: Generalized linear models implementation 
code: def fit(self, input_data):
      self.model = GLM(
          exog=sm.add_constant(
              input_data.idx.astype("float64")
          ).reshape(-1, 2),
          endog=input_data.target.astype("float64").reshape(-1, 1),
          family=self.family_link,
      ).fit(method="lbfgs")
      return self.model
  
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 4 for calling the GLMImplementation obj's underlying member method code with example usage: obj.fit(input_data) and returns: self