class_docstr: Multivariate transformer derived from a user-given function and parameters.
  
  Parameters
  ----------
  Parameters
  ----------
  transform_func: function
      A function transforming multivariate time series.
  
      The first input argument must be a pandas DataFrame, optional input
      argument may be accepted through parameter `transform_func_params` and
      the output of `fit_func`, and the output must be a pandas Series or
      DataFrame with the same index as input.
  
  transform_func_params: dict, optional
      Parameters of `transform_func`. Default: None.
  
  fit_func: function, optional
      A function training parameters of `transform_func` with multivariate
      time series.
  
      The first input argument must be a pandas DataFrame, optional input
      argument may be accepted through parameter `fit_func_params`, and the
      output must be a dict that can be used by `transform_func` as
      parameters. Default: None.
  
  fit_func_params: dict, optional
      Parameters of `fit_func`. Default: None.
code: def _fit_core(self, df: pd.DataFrame) -> None:
      if self.fit_func is not None:
          if self.fit_func_params is not None:
              fit_func_params = self.fit_func_params
          else:
              fit_func_params = {}
          self._fitted_transform_func_params = self.fit_func(
              df, **fit_func_params
          )
  
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 3 for calling the CustomizedTransformerHD obj's underlying member method code with example usage: obj._fit_core(df) without return types