class_docstr: Compute power spectral density (PSD) using a multi-taper method.
  
  Parameters
  ----------
  sfreq : float
      The sampling frequency.
  fmin : float
      The lower frequency of interest.
  fmax : float
      The upper frequency of interest.
  bandwidth : float
      The bandwidth of the multi taper windowing function in Hz.
  adaptive : bool
      Use adaptive weights to combine the tapered spectra into PSD
      (slow, use n_jobs >> 1 to speed up computation).
  low_bias : bool
      Only use tapers with more than 90%% spectral concentration within
      bandwidth.
  n_jobs : int
      Number of parallel jobs to use (only used if adaptive=True).
  %(normalization)s
  %(verbose)s
  
  See Also
  --------
  mne.time_frequency.psd_array_multitaper
  mne.io.Raw.compute_psd
  mne.Epochs.compute_psd
  mne.Evoked.compute_psd
code: def __init__(
      self,
      sfreq=2 * np.pi,
      fmin=0,
      fmax=np.inf,
      bandwidth=None,
      adaptive=False,
      low_bias=True,
      n_jobs=None,
      normalization="length",
      *,
      verbose=None,
  ):
      self.sfreq = sfreq
      self.fmin = fmin
      self.fmax = fmax
      self.bandwidth = bandwidth
      self.adaptive = adaptive
      self.low_bias = low_bias
      self.n_jobs = n_jobs
      self.normalization = normalization
  
imports: import numpy
  from .._fiff.pick import _pick_data_channels, _picks_by_type, _picks_to_idx, pick_info, pick_types
  from ..cov import _check_scalings_user
  from ..filter import filter_data
  from ..time_frequency import psd_array_multitaper
  from ..utils import _check_option, _validate_type, fill_doc, verbose
  from .base import BaseEstimator
  from .mixin import TransformerMixin
label_desc: Use image node_id 1 to create a new PSDEstimator object from inherited base classes: TransformerMixin with example: obj = PSDEstimator(sfreq, fmin, fmax, bandwidth, adaptive, low_bias, n_jobs, normalization)