class_docstr: Exponential smoothing implementation from statsmodels 
code: def __init__(self, params: OperationParameters):
      super().__init__(params)
      self.model = None
      if self.params.get("seasonal"):
          self.seasonal_periods = int(
              self.params.get("seasonal_periods")
          )
      else:
          self.seasonal_periods = None
  
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 1 to create a new ExpSmoothingImplementation object from inherited base classes: ModelImplementation with example: obj = ExpSmoothingImplementation(params)