code: def wiener(im, mysize=None, noise=None):
      """
      Perform a Wiener filter on an N-dimensional array.
  
      Apply a Wiener filter to the N-dimensional array `im`.
  
      Parameters
      ----------
      im : ndarray
          An N-dimensional array.
      mysize : int or array_like, optional
          A scalar or an N-length list giving the size of the Wiener filter
          window in each dimension.  Elements of mysize should be odd.
          If mysize is a scalar, then this scalar is used as the size
          in each dimension.
      noise : float, optional
          The noise-power to use. If None, then noise is estimated as the
          average of the local variance of the input.
  
      Returns
      -------
      out : ndarray
          Wiener filtered result with the same shape as `im`.
  
      Examples
      --------
  
      >>> from scipy.misc import face
      >>> from scipy.signal.signaltools import wiener
      >>> import matplotlib.pyplot as plt
      >>> import numpy as np
      >>> rng = np.random.default_rng()
      >>> img = rng.random((40, 40))    #Create a random image
      >>> filtered_img = wiener(img, (5, 5))  #Filter the image
      >>> f, (plot1, plot2) = plt.subplots(1, 2)
      >>> plot1.imshow(img)
      >>> plot2.imshow(filtered_img)
      >>> plt.show()
  
      Notes
      -----
      This implementation is similar to wiener2 in Matlab/Octave.
      For more details see [1]_
  
      References
      ----------
      .. [1] Lim, Jae S., Two-Dimensional Signal and Image Processing,
             Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548.
  
  
      """
      im = np.asarray(im)
      if mysize is None:
          mysize = [3] * im.ndim
      mysize = np.asarray(mysize)
      if mysize.shape == ():
          mysize = np.repeat(mysize.item(), im.ndim)
  
      # Estimate the local mean
      lMean = correlate(im, np.ones(mysize), "same") / np.prod(
          mysize, axis=0
      )
  
      # Estimate the local variance
      lVar = (
          correlate(im**2, np.ones(mysize), "same")
          / np.prod(mysize, axis=0)
          - lMean**2
      )
  
      # Estimate the noise power if needed.
      if noise is None:
          noise = np.mean(np.ravel(lVar), axis=0)
  
      res = im - lMean
      res *= 1 - noise / lVar
      res += lMean
      out = np.where(lVar < noise, lMean, res)
  
      return out
  
imports: import operator
  import math
  import timeit
  from scipy.spatial import cKDTree
  from .None import sigtools, dlti
  from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
  from scipy import linalg, fft
  from scipy.fft._helper import _init_nd_shape_and_axes
  from scipy._lib._util import prod
  import numpy
  from scipy.special import lambertw
  from .windows import get_window
  from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
  from .filter_design import cheby1, _validate_sos
  from .fir_filter_design import firwin
  from ._sosfilt import _sosfilt
  import warnings
label_desc: Use image node_id 23 for calling a global function with example usage: wiener(im, mysize, noise) and returns: out