class_docstr: Class to hold results from fitting an VARMAX model.
  
  Parameters
  ----------
  model : VARMAX instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the VARMAX model instance.
  coefficient_matrices_var : ndarray
      Array containing autoregressive lag polynomial coefficient matrices,
      ordered from lowest degree to highest.
  coefficient_matrices_vma : ndarray
      Array containing moving average lag polynomial coefficients,
      ordered from lowest degree to highest.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
code: def get_prediction(
      self,
      start=None,
      end=None,
      dynamic=False,
      information_set="predicted",
      index=None,
      exog=None,
      **kwargs,
  ):
      if start is None:
          start = 0
  
      # Handle end (e.g. date)
      _start, _end, out_of_sample, _ = self.model._get_prediction_index(
          start, end, index, silent=True
      )
  
      # Normalize `exog`
      exog = self.model._validate_out_of_sample_exog(
          exog, out_of_sample
      )
  
      # Handle trend offset for extended model
      extend_kwargs = {}
      if self.model.k_trend > 0:
          extend_kwargs["trend_offset"] = (
              self.model.trend_offset + self.nobs
          )
  
      # Get the prediction
      with self._set_final_exog(exog):
          with self._set_final_predicted_state(exog, out_of_sample):
              out = super().get_prediction(
                  start=start,
                  end=end,
                  dynamic=dynamic,
                  information_set=information_set,
                  index=index,
                  exog=exog,
                  extend_kwargs=extend_kwargs,
                  **kwargs,
              )
      return out
  
imports: import contextlib
  from warnings import warn
  import pandas
  import numpy
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.vector_ar import var_model
  import statsmodels.base.wrapper
  from statsmodels.tools.sm_exceptions import EstimationWarning
  from .kalman_filter import INVERT_UNIVARIATE, SOLVE_LU
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .initialization import Initialization
  from .tools import is_invertible, concat, prepare_exog, constrain_stationary_multivariate, unconstrain_stationary_multivariate, prepare_trend_spec, prepare_trend_data
label_desc: Use image node_id 5 for calling the VARMAXResults obj's underlying member method code with example usage: obj.get_prediction(start, end, dynamic, information_set, index, exog) and returns: out