class_docstr: Exponential smoothing implementation from statsmodels 
code: def fit(self, input_data):
      self.model = ETSModel(
          input_data.features.astype("float64"),
          error=self.params.get("error"),
          trend=self.params.get("trend"),
          seasonal=self.params.get("seasonal"),
          damped_trend=self.params.get("damped_trend")
          if self.params.get("trend")
          else None,
          seasonal_periods=self.seasonal_periods,
      )
      self.model = self.model.fit(disp=False)
      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 2 for calling the ExpSmoothingImplementation obj's underlying member method code with example usage: obj.fit(input_data) and returns: self