code: def _inverse_transform(pca, data):
      """
      Inverse transform on PCA.
  
      Use PCA's `project` method by temporary replacing its factors with
      `data`.
  
      Parameters
      ----------
      pca : statsmodels Principal Component Analysis instance
          The PCA object to use.
      data : sequence of ndarrays or 2-D ndarray
          The vectors of functions to create a functional boxplot from.  If a
          sequence of 1-D arrays, these should all be the same size.
          The first axis is the function index, the second axis the one along
          which the function is defined.  So ``data[0, :]`` is the first
          functional curve.
  
      Returns
      -------
      projection : ndarray
          nobs by nvar array of the projection onto ncomp factors
      """
      factors = pca.factors
      pca.factors = data.reshape(-1, factors.shape[1])
      projection = pca.project()
      pca.factors = factors
      return projection
  
file_path: statsmodels/statsmodels/graphics/functional.py
imports: from statsmodels.compat.numpy import NP_LT_123
  import numpy
  from scipy.special import comb
  from statsmodels.graphics.utils import _import_mpl
  from statsmodels.multivariate.pca import PCA
  from statsmodels.nonparametric.kernel_density import KDEMultivariate
  import itertools
  from multiprocessing import Pool
  from .None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: _inverse_transform(pca, data) and returns: projection