code: def _prep_for_filtering(x, copy, picks=None):
      """Set up array as 2D for filtering ease."""
      x = _check_filterable(x)
      if copy is True:
          x = x.copy()
      orig_shape = x.shape
      x = np.atleast_2d(x)
      picks = _picks_to_idx(x.shape[-2], picks)
      x.shape = (np.prod(x.shape[:-1]), x.shape[-1])
      if len(orig_shape) == 3:
          n_epochs, n_channels, n_times = orig_shape
          offset = np.repeat(
              np.arange(0, n_channels * n_epochs, n_channels),
              len(picks),
          )
          picks = np.tile(picks, n_epochs) + offset
      elif len(orig_shape) > 3:
          raise ValueError(
              "picks argument is not supported for data with more"
              " than three dimensions"
          )
      assert all(
          0 <= pick < x.shape[0] for pick in picks
      )  # guaranteed by above
  
      return x, orig_shape, picks
  
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 6 for calling a global function with example usage: _prep_for_filtering(x, copy, picks) and returns: x,  orig_shape,  picks