class_docstr: Discrete Wavelet Transform Transformer.
  
  Performs the Haar wavelet transformation on a time series.
  
  Parameters
  ----------
  num_levels : int, number of levels to perform the Haar wavelet
               transformation.
class_docstr_tok: ["Discrete","Wavelet","Transform","Transformer",".","Performs","the","Haar","wavelet","transformation","on","a","time","series",".","Parameters","--","--","--","--","--","num_levels",":","int",",","number","of","levels","to","perform","the","Haar","wavelet","transformation","."]
docstr: Check the values of parameters passed to DWT.
  
  Throws
  ------
  ValueError or TypeError if a parameters input is invalid.
docstr_tok: ["Check","the","values","of","parameters","passed","to","DWT",".","Throws","--","--","--","ValueError","or","TypeError","if","a","parameters","input","is","invalid","."]
code: def _check_parameters(self):
      """Check the values of parameters passed to DWT.
  
      Throws
      ------
      ValueError or TypeError if a parameters input is invalid.
      """
      if isinstance(self.num_levels, int):
          if self.num_levels <= -1:
              raise ValueError(
                  "num_levels must have the value" + "of at least 0"
              )
      else:
          raise TypeError(
              "num_levels must be an 'int'. Found"
              + "'"
              + type(self.num_levels).__name__
              + "' instead."
          )
  
file_path: sktime/sktime/transformations/panel/dwt.py
imports: import math
  import numpy
  import pandas
  from sktime.datatypes import convert
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 4 for calling the DWTTransformer obj's underlying member method code with example usage: obj._check_parameters() without return types