class_docstr: Results from fitting a linear exponential smoothing model
code: def summary(self, alpha=0.05, start=None):
      specification = ["A"]
      if self.model.trend and self.model.damped_trend:
          specification.append("Ad")
      elif self.model.trend:
          specification.append("A")
      else:
          specification.append("N")
      if self.model.seasonal:
          specification.append("A")
      else:
          specification.append("N")
  
      model_name = "ETS(" + ", ".join(specification) + ")"
  
      summary = super().summary(
          alpha=alpha,
          start=start,
          title="Exponential Smoothing Results",
          model_name=model_name,
      )
  
      if self.model.initialization_method != "estimated":
          params = np.array(self.initial_state)
          if params.ndim > 1:
              params = params[0]
          names = self.model.state_names[1:]
          param_header = [
              "initialization method: %s"
              % self.model.initialization_method
          ]
          params_stubs = names
          params_data = [
              [forg(params[i], prec=4)] for i in range(len(params))
          ]
  
          initial_state_table = SimpleTable(
              params_data,
              param_header,
              params_stubs,
              txt_fmt=fmt_params,
          )
          summary.tables.insert(-1, initial_state_table)
  
      return summary
  
imports: import numpy
  import pandas
  from statsmodels.base.data import PandasData
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.tools.validation import array_like, bool_like, float_like, string_like, int_like
  from statsmodels.tsa.exponential_smoothing import initialization
  from statsmodels.tsa.statespace import initialization
  from statsmodels.tsa.statespace.kalman_filter import MEMORY_CONSERVE, MEMORY_NO_FORECAST
  from statsmodels.compat.pandas import Appender
  import statsmodels.base.wrapper
  from statsmodels.iolib.summary import forg
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.iolib.tableformatting import fmt_params
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
label_desc: Use image node_id 2 for calling the ExponentialSmoothingResults obj's underlying member method code with example usage: obj.summary(alpha, start) and returns: summary