docstr: Class fit STLForecast arima model on data
  
  :param input_data: data with features, target and ids to process
docstr_tok: ["Class","fit","STLForecast","arima","model","on","data",":","param","input_data",":","data","with","features",",","target","and","ids","to","process"]
code: def fit(self, input_data):
      """Class fit STLForecast arima model on data
  
      :param input_data: data with features, target and ids to process
      """
  
      source_ts = np.array(input_data.features)
      # Save actual time series length
      self.actual_ts_len = len(source_ts)
  
      p = int(self.params.setdefault("p", 2))
      d = int(self.params.setdefault("d", 0))
      q = int(self.params.setdefault("q", 2))
      period = int(self.params.setdefault("period", 365))
      params = {"period": period, "model_kwargs": {"order": (p, d, q)}}
      self.model = STLForecast(source_ts, ARIMA, **params).fit()
  
      return self.model
  
imports: from copy import copy
  from typing import Optional
  import numpy
  from scipy import stats
  from scipy.special import boxcox, inv_boxcox
  from statsmodels.tsa.api import STLForecast
  from statsmodels.tsa.arima.model import ARIMA
  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
  from fedot.utilities.ts_gapfilling import SimpleGapFiller
label_desc: Use image node_id 2 for calling the STLForecastARIMAImplementation obj's underlying member method code with example usage: obj.fit(input_data) and returns: self