class_docstr: Results class for generalized additive models, GAM.
  
  This inherits from GLMResults.
  
  Warning: some inherited methods might not correctly take account of the
  penalization
  
  GLMGamResults inherits from GLMResults
  All methods related to the loglikelihood function return the penalized
  values.
  
  Attributes
  ----------
  
  edf
      list of effective degrees of freedom for each column of the design
      matrix.
  hat_matrix_diag
      diagonal of hat matrix
  gcv
      generalized cross-validation criterion computed as
      ``gcv = scale / (1. - hat_matrix_trace / self.nobs)**2``
  cv
      cross-validation criterion computed as
      ``cv = ((resid_pearson / (1 - hat_matrix_diag))**2).sum() / nobs``
  
  Notes
  -----
  status: experimental
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docstr: compute prediction results
  
  Parameters
  ----------
  exog : array_like, optional
      The values for which you want to predict.
  exog_smooth : array_like
      values for the variables in the smooth terms
  transform : bool, optional
      If transform is True, then the basis representation of the smooth
      term will be constructed from the provided ``x``.
  kwargs :
      Some models can take additional arguments or keywords, see the
      predict method of the model for the details.
  
  Returns
  -------
  prediction_results : generalized_linear_model.PredictionResults
      The prediction results instance contains prediction and prediction
      variance and can on demand calculate confidence intervals and
      summary tables for the prediction of the mean and of new
      observations.
docstr_tok: ["compute","prediction","results","Parameters","--","--","--","--","--","exog",":","array_like",",","optional","The","values","for","which","you","want","to","predict",".","exog_smooth",":","array_like","values","for","the","variables","in","the","smooth","terms","transform",":","bool",",","optional","If","transform","is","True",",","then","the","basis","representation","of","the","smooth","term","will","be","constructed","from","the","provided","``","x","``",".","kwargs",":","Some","models","can","take","additional","arguments","or","keywords",",","see","the","predict","method","of","the","model","for","the","details",".","Returns","--","--","--","-","prediction_results",":","generalized_linear_model.PredictionResults","The","prediction","results","instance","contains","prediction","and","prediction","variance","and","can","on","demand","calculate","confidence","intervals","and","summary","tables","for","the","prediction","of","the","mean","and","of","new","observations","."]
code: def get_prediction(
      self, exog=None, exog_smooth=None, transform=True, **kwargs
  ):
      """compute prediction results
  
      Parameters
      ----------
      exog : array_like, optional
          The values for which you want to predict.
      exog_smooth : array_like
          values for the variables in the smooth terms
      transform : bool, optional
          If transform is True, then the basis representation of the smooth
          term will be constructed from the provided ``x``.
      kwargs :
          Some models can take additional arguments or keywords, see the
          predict method of the model for the details.
  
      Returns
      -------
      prediction_results : generalized_linear_model.PredictionResults
          The prediction results instance contains prediction and prediction
          variance and can on demand calculate confidence intervals and
          summary tables for the prediction of the mean and of new
          observations.
      """
      ex, exog_index = self._tranform_predict_exog(
          exog=exog, exog_smooth=exog_smooth, transform=transform
      )
      return super().get_prediction(ex, transform=False, **kwargs)
  
imports: from collections.abc import Iterable
  import copy
  import numpy
  from scipy import optimize
  import pandas
  import statsmodels.base.wrapper
  from statsmodels.discrete.discrete_model import Logit
  from statsmodels.genmod.generalized_linear_model import GLM, GLMResults, GLMResultsWrapper, _check_convergence
  import statsmodels.regression.linear_model
  from statsmodels.tools.sm_exceptions import PerfectSeparationError, ValueWarning
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tools.linalg import matrix_sqrt
  from statsmodels.base._penalized import PenalizedMixin
  from statsmodels.gam.gam_penalties import MultivariateGamPenalty
  from statsmodels.gam.gam_cross_validation.gam_cross_validation import MultivariateGAMCVPath
  from statsmodels.gam.gam_cross_validation.cross_validators import KFold
label_desc: Use image node_id 4 for calling the GLMGamResults obj's underlying member method code with example usage: obj.get_prediction(exog, exog_smooth, transform) and returns: super