code: def _fit_full(self, X, n_components):
      n_samples, n_features = X.shape
      self._validate_n_components(n_components, n_samples, n_features)
  
      self._fit_full_daal4py(X, min(X.shape))
  
      U = None
      V = self.components_
      S = self.singular_values_
  
      if n_components == "mle":
          if sklearn_check_version("0.23"):
              n_components = _infer_dimension(
                  self.explained_variance_, n_samples
              )
          else:
              n_components = _infer_dimension_(
                  self.explained_variance_, n_samples, n_features
              )
      elif 0 < n_components < 1.0:
          ratio_cumsum = stable_cumsum(self.explained_variance_ratio_)
          n_components = (
              np.searchsorted(ratio_cumsum, n_components, side="right")
              + 1
          )
  
      if n_components < min(n_features, n_samples):
          self.noise_variance_ = self.explained_variance_[
              n_components:
          ].mean()
      else:
          self.noise_variance_ = 0.0
  
      if sklearn_check_version("1.2"):
          self.n_samples_, self.n_features_in_ = n_samples, n_features
      else:
          self.n_samples_, self.n_features_ = n_samples, n_features
      self.components_ = self.components_[:n_components]
      self.n_components_ = n_components
      self.explained_variance_ = self.explained_variance_[:n_components]
      self.explained_variance_ratio_ = self.explained_variance_ratio_[
          :n_components
      ]
      self.singular_values_ = self.singular_values_[:n_components]
  
      return U, S, V
  
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 4 for calling the PCA obj's underlying member method code with example usage: obj._fit_full(X, n_components) and returns: U,  S,  V