class_docstr: Transformer that performs principal component analysis (PCA) to the
  multivariate time series  (every time point is treated as a point in high-
  dimensional space), and reconstructs those points with the first k
  principal components.
  
  Parameters
  ----------
  k: int, optional
      Number of principal components to use. Default: 1.
code: def __init__(self, k: int = 1) -> None:
      self._model = None  # type: Any
      super().__init__()
      self.k = k
  
imports: from typing import Any, Callable, Dict, Optional, Tuple, Union
  import pandas
  from sklearn.decomposition import PCA
  from .._transformer_base import _NonTrainableMultivariateTransformer, _TrainableMultivariateTransformer
label_desc: Use image node_id 1 to create a new PcaReconstruction object from inherited base classes: _TrainableMultivariateTransformer with example: obj = PcaReconstruction(k)