class_docstr: Base class for SparsePCA and MiniBatchSparsePCA
class_docstr_tok: ["Base","class","for","SparsePCA","and","MiniBatchSparsePCA"]
docstr: Number of transformed output features.
docstr_tok: ["Number","of","transformed","output","features","."]
code: def _n_features_out(self):
      """Number of transformed output features."""
      return self.components_.shape[0]
  
imports: from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from ..linear_model import ridge_regression
  from ..utils import check_random_state
  from ..utils._param_validation import Hidden, Interval, StrOptions
  from ..utils.extmath import svd_flip
  from ..utils.validation import check_array, check_is_fitted
  from ._dict_learning import MiniBatchDictionaryLearning, dict_learning
label_desc: Use image node_id 5 for calling the _BaseSparsePCA obj's underlying member method code with example usage: obj._n_features_out() and returns: self