class_docstr: Transformer that filters Series data.
  
  FIR, IIR, band pass filters.
  
  Provides a simple wrapper around ``mne.filter.filter_data``.
  
  Parameters
  ----------
  sfreq: int or float
      sampling frequency of the recorded data in Hz
  l_freq: float or None
      For FIR filters, the lower pass-band edge;
      for IIR filters, the lower cutoff frequency.
      If None the data are only low-passed.
  h_freq: float or None
      For FIR filters, the upper pass-band edge;
      for IIR filters, the upper cutoff frequency.
      If None the data are only high-passed.
  filter_kwargs: dict or None
      Additional parameters passed on to ``mne.filter.filter_data``.
      See ``mne.filter.filter_data``
      documentation for a detailed description of all options.
code: def __init__(
      self,
      sfreq,
      l_freq=None,
      h_freq=None,
      filter_kwargs=None,
  ):
      self.sfreq = sfreq
      self.l_freq = l_freq
      self.h_freq = h_freq
      self.filter_kwargs = filter_kwargs
      if not (
          isinstance(sfreq, (int, float))
          & isinstance(l_freq, (int, float, type(None)))
          & isinstance(h_freq, (int, float, type(None)))
      ):
          raise TypeError
      elif (l_freq is not None) & (h_freq is not None):
          if not ((l_freq > 0) & (h_freq > 0)):
              raise ValueError("Negative values not supported")
          if l_freq > h_freq:
              raise ValueError(
                  "High frequency must be higher" " than low frequency"
              )
      super().__init__()
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new Filter object from inherited base classes: BaseTransformer with example: obj = Filter(sfreq, l_freq, h_freq, filter_kwargs)