docstr: Class fit ar model on data
  
  :param input_data: data with features, target and ids to process
code: def fit(self, input_data):
      """Class fit ar model on data
  
      :param input_data: data with features, target and ids to process
      """
  
      source_ts = np.array(input_data.features)
      self.actual_ts_len = len(source_ts)
  
      # Correct window size parameter
      self._check_and_correct_lags(source_ts)
  
      lag_1 = int(self.params.get("lag_1"))
      lag_2 = int(self.params.get("lag_2"))
      self.autoreg = AutoReg(source_ts, lags=[lag_1, lag_2]).fit()
      self.actual_ts_len = input_data.idx.shape[0]
  
      return self.autoreg
  
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 2 for calling the AutoRegImplementation obj's underlying member method code with example usage: obj.fit(input_data) and returns: self