class_docstr: Object for Epoch/Evoked filtering.
docstr: Resample data.
  
  If appropriate, an anti-aliasing filter is applied before resampling.
  See :ref:`resampling-and-decimating` for more information.
  
  .. note:: Data must be loaded.
  
  Parameters
  ----------
  sfreq : float
      New sample rate to use.
  %(npad)s
  %(window_resample)s
  %(n_jobs_cuda)s
  %(pad_resample)s
  
      .. versionadded:: 0.15
  %(method_resample)s
  
      .. versionadded:: 1.7
  %(verbose)s
  
  Returns
  -------
  inst : instance of Epochs or Evoked
      The resampled object.
  
  See Also
  --------
  mne.io.Raw.resample
  
  Notes
  -----
  For some data, it may be more accurate to use npad=0 to reduce
  artifacts. This is dataset dependent -- check your data!
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code: def resample(
      self,
      sfreq,
      *,
      npad="auto",
      window="auto",
      n_jobs=None,
      pad="edge",
      method="fft",
      verbose=None,
  ):
      """Resample data.
  
      If appropriate, an anti-aliasing filter is applied before resampling.
      See :ref:`resampling-and-decimating` for more information.
  
      .. note:: Data must be loaded.
  
      Parameters
      ----------
      sfreq : float
          New sample rate to use.
      %(npad)s
      %(window_resample)s
      %(n_jobs_cuda)s
      %(pad_resample)s
  
          .. versionadded:: 0.15
      %(method_resample)s
  
          .. versionadded:: 1.7
      %(verbose)s
  
      Returns
      -------
      inst : instance of Epochs or Evoked
          The resampled object.
  
      See Also
      --------
      mne.io.Raw.resample
  
      Notes
      -----
      For some data, it may be more accurate to use npad=0 to reduce
      artifacts. This is dataset dependent -- check your data!
      """
      from .epochs import BaseEpochs
      from .evoked import Evoked
  
      # Should be guaranteed by our inheritance, and the fact that
      # mne.io.BaseRaw overrides this method
      assert isinstance(self, (BaseEpochs, Evoked))
  
      sfreq = float(sfreq)
      o_sfreq = self.info["sfreq"]
      if _check_resamp_noop(sfreq, o_sfreq):
          return self
  
      _check_preload(self, "inst.resample")
      self._data = resample(
          self._data,
          sfreq,
          o_sfreq,
          npad=npad,
          window=window,
          n_jobs=n_jobs,
          pad=pad,
          method=method,
      )
      lowpass = self.info.get("lowpass")
      lowpass = np.inf if lowpass is None else lowpass
      with self.info._unlock():
          self.info["lowpass"] = min(lowpass, sfreq / 2.0)
          self.info["sfreq"] = float(sfreq)
      new_times = (
          np.arange(self._data.shape[-1], dtype=np.float64) / sfreq
          + self.times[0]
      )
      # adjust indirectly affected variables
      self._set_times(new_times)
      self._raw_times = self.times
      self._update_first_last()
      return self
  
imports: from collections import Counter
  from copy import deepcopy
  from functools import partial
  from math import gcd
  import numpy
  from scipy import fft, signal
  from scipy.stats import f
  from ._fiff.pick import _picks_to_idx
  from ._ola import _COLA
  from .cuda import _fft_multiply_repeated, _fft_resample, _setup_cuda_fft_multiply_repeated, _setup_cuda_fft_resample, _smart_pad
  from .parallel import parallel_func
  from .utils import _check_option, _check_preload, _ensure_int, _pl, _validate_type, logger, sum_squared, verbose, warn
label_desc: Use image node_id 3 for calling the FilterMixin obj's underlying member method code with example usage: obj.resample(sfreq) and returns: self, self