code: def _transform_daal4py(
      self, X, whiten=False, scale_eigenvalues=True, check_X=True
  ):
      if sklearn_check_version("0.22"):
          check_is_fitted(self)
      else:
          check_is_fitted(
              self, ["mean_", "components_"], all_or_any=all
          )
  
      if sklearn_check_version("1.0"):
          self._check_feature_names(X, reset=False)
      X = check_array(
          X, dtype=[np.float64, np.float32], force_all_finite=check_X
      )
      fpType = getFPType(X)
  
      tr_data = dict()
      if self.mean_ is not None:
          tr_data["mean"] = self.mean_.reshape((1, -1))
      if whiten:
          if scale_eigenvalues:
              tr_data["eigenvalue"] = (
                  self.n_samples_ - 1
              ) * self.explained_variance_.reshape((1, -1))
          else:
              tr_data["eigenvalue"] = self.explained_variance_.reshape(
                  (1, -1)
              )
      elif scale_eigenvalues:
          tr_data["eigenvalue"] = np.full(
              (1, self.explained_variance_.shape[0]),
              self.n_samples_ - 1.0,
              dtype=X.dtype,
          )
  
      if sklearn_check_version("1.2"):
          expected_n_features = self.n_features_in_
      else:
          expected_n_features = self.n_features_
      if X.shape[1] != expected_n_features:
          raise ValueError(
              (
                  f"X has {X.shape[1]} features, "
                  f"but PCA is expecting {expected_n_features} features as input"
              )
          )
  
      tr_res = daal4py.pca_transform(fptype=fpType).compute(
          X, self.components_, tr_data
      )
  
      return tr_res.transformedData
  
file_path: scikit-learn-intelex/daal4py/sklearn/decomposition/_pca.py
imports: import numbers
  from math import sqrt
  import numpy
  from scipy.sparse import issparse
  from sklearn.utils import check_array
  from sklearn.utils.extmath import stable_cumsum
  from sklearn.utils.validation import check_is_fitted
  import daal4py
  from .._device_offload import support_usm_ndarray
  from .._utils import PatchingConditionsChain, control_n_jobs, getFPType, run_with_n_jobs, sklearn_check_version
label_desc: Use image node_id 6 for calling the PCA obj's underlying member method code with example usage: obj._transform_daal4py(X, whiten, scale_eigenvalues, check_X) and returns: tr_res