parameters: (input, kernel_size=3)
return_statement: return sigtools._medfilt2d(image, kernel_size)
docstring: Median filter a 2-dimensional array.
  
      Apply a median filter to the `input` array using a local window-size
      given by `kernel_size` (must be odd).
  
      Parameters
      ----------
      input : array_like
          A 2-dimensional input array.
      kernel_size : array_like, optional
          A scalar or a list of length 2, giving the size of the
          median filter window in each dimension.  Elements of
          `kernel_size` should be odd.  If `kernel_size` is a scalar,
          then this scalar is used as the size in each dimension.
          Default is a kernel of size (3, 3).
  
      Returns
      -------
      out : ndarray
          An array the same size as input containing the median filtered
          result.
docstring_summary: Median filter a 2-dimensional array.
docstring_tokens: Median filter a 2 - dimensional array .
function: def medfilt2d(input, kernel_size=3):
      """
      Median filter a 2-dimensional array.
  
      Apply a median filter to the `input` array using a local window-size
      given by `kernel_size` (must be odd).
  
      Parameters
      ----------
      input : array_like
          A 2-dimensional input array.
      kernel_size : array_like, optional
          A scalar or a list of length 2, giving the size of the
          median filter window in each dimension.  Elements of
          `kernel_size` should be odd.  If `kernel_size` is a scalar,
          then this scalar is used as the size in each dimension.
          Default is a kernel of size (3, 3).
  
      Returns
      -------
      out : ndarray
          An array the same size as input containing the median filtered
          result.
  
      """
      image = asarray(input)
      if kernel_size is None:
          kernel_size = [3] * 2
      kernel_size = asarray(kernel_size)
      if kernel_size.shape == ():
          kernel_size = np.repeat(kernel_size.item(), 2)
  
      for size in kernel_size:
          if (size % 2) != 1:
              raise ValueError("Each element of kernel_size should be odd.")
  
      return sigtools._medfilt2d(image, kernel_size)
function_tokens: def medfilt2d ( input , kernel_size = 3 ) : image = asarray ( input ) if kernel_size is None : kernel_size = [ 3 ] * 2 kernel_size = asarray ( kernel_size ) if kernel_size . shape == ( ) : kernel_size = np . repeat ( kernel_size . item ( ) , 2 ) for size in kernel_size : if ( size % 2 ) != 1 : raise ValueError ( "Each element of kernel_size should be odd." ) return sigtools . _medfilt2d ( image , kernel_size )