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
docstr: Compute power spectral density (PSD) using a multi-taper method.
  
  Parameters
  ----------
  epochs_data : array, shape (n_epochs, n_channels, n_times)
      The data.
  y : array, shape (n_epochs,)
      The label for each epoch.
  
  Returns
  -------
  self : instance of PSDEstimator
      The modified instance.
docstr_tok: ["Compute","power","spectral","density","(","PSD",")","using","a","multi-taper","method",".","Parameters","--","--","--","--","--","epochs_data",":","array",",","shape","(","n_epochs",",","n_channels",",","n_times",")","The","data",".","y",":","array",",","shape","(","n_epochs",",",")","The","label","for","each","epoch",".","Returns","--","--","--","-","self",":","instance","of","PSDEstimator","The","modified","instance","."]
code: def fit(self, epochs_data, y):
      """Compute power spectral density (PSD) using a multi-taper method.
  
      Parameters
      ----------
      epochs_data : array, shape (n_epochs, n_channels, n_times)
          The data.
      y : array, shape (n_epochs,)
          The label for each epoch.
  
      Returns
      -------
      self : instance of PSDEstimator
          The modified instance.
      """
      if not isinstance(epochs_data, np.ndarray):
          raise ValueError(
              "epochs_data should be of type ndarray (got %s)."
              % type(epochs_data)
          )
  
      return self
  
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 2 for calling the PSDEstimator obj's underlying member method code with example usage: obj.fit(epochs_data, y) and returns: self