class_docstr: Meta-transformer for selecting continuous features and transform them using PCA.
  
  Parameters
  ----------
  
  threshold : int, default=10
      Maximum number of unique values per feature to consider the feature
      to be categorical.
  
  svd_solver : string {'auto', 'full', 'arpack', 'randomized'}
      auto :
          the solver is selected by a default policy based on `X.shape` and
          `n_components`: if the input data is larger than 500x500 and the
          number of components to extract is lower than 80% of the smallest
          dimension of the data, then the more efficient 'randomized'
          method is enabled. Otherwise the exact full SVD is computed and
          optionally truncated afterwards.
      full :
          run exact full SVD calling the standard LAPACK solver via
          `scipy.linalg.svd` and select the components by postprocessing
      arpack :
          run SVD truncated to n_components calling ARPACK solver via
          `scipy.sparse.linalg.svds`. It requires strictly
          0 < n_components < X.shape[1]
      randomized :
          run randomized SVD by the method of Halko et al.
  
  iterated_power : int >= 0, or 'auto', (default 'auto')
      Number of iterations for the power method computed by
      svd_solver == 'randomized'.
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docstr: Select continuous features and transform them using PCA.
  
  Parameters
  ----------
  X: numpy ndarray, {n_samples, n_components}
      New data, where n_samples is the number of samples and n_components is the number of components.
  
  Returns
  -------
  array-like, {n_samples, n_components}
docstr_tok: ["Select","continuous","features","and","transform","them","using","PCA",".","Parameters","--","--","--","--","--","X",":","numpy","ndarray",",","{","n_samples",",","n_components","}","New","data",",","where","n_samples","is","the","number","of","samples","and","n_components","is","the","number","of","components",".","Returns","--","--","--","-","array-like",",","{","n_samples",",","n_components","}"]
code: def transform(self, X):
      """Select continuous features and transform them using PCA.
  
      Parameters
      ----------
      X: numpy ndarray, {n_samples, n_components}
          New data, where n_samples is the number of samples and n_components is the number of components.
  
      Returns
      -------
      array-like, {n_samples, n_components}
      """
      selected = auto_select_categorical_features(
          X, threshold=self.threshold
      )
      _, X_sel, n_selected, _ = _X_selected(X, selected)
  
      if n_selected == 0:
          # No features selected.
          raise ValueError("No continuous feature was found!")
      else:
          pca = PCA(
              svd_solver=self.svd_solver,
              iterated_power=self.iterated_power,
              random_state=self.random_state,
          )
          return pca.fit_transform(X_sel)
  
file_path: tpot/tpot/builtins/feature_transformers.py
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.decomposition import PCA
  from .one_hot_encoder import OneHotEncoder, auto_select_categorical_features, _X_selected
label_desc: Use image node_id 3 for calling the ContinuousSelector obj's underlying member method code with example usage: obj.transform(X) and returns: pca