code: def get_scalings_for_boundary_condition(self, sigma):
      c_skip = self.sigma_data**2 / (
          (sigma - self.sigma_min) ** 2 + self.sigma_data**2
      )
      c_out = (
          (sigma - self.sigma_min)
          * self.sigma_data
          / (sigma**2 + self.sigma_data**2) ** 0.5
      )
      c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
      return c_skip, c_out, c_in
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from piq import LPIPS
  from torchvision.transforms import RandomCrop
  from .None import dist_util
  from .nn import mean_flat, append_dims, append_zero
  from .random_util import get_generator
label_desc: Use image node_id 5 for calling the KarrasDenoiser obj's underlying member method code with example usage: obj.get_scalings_for_boundary_condition(sigma) and returns: c_skip,  c_out,  c_in