code: def __call__(
      self, sigma: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
      c_skip = torch.ones_like(sigma, device=sigma.device)
      c_out = -sigma
      c_in = 1 / (sigma**2 + 1.0) ** 0.5
      c_noise = sigma.clone()
      return c_skip, c_out, c_in, c_noise
  
file_path: generative-models/sgm/modules/diffusionmodules/denoiser_scaling.py
imports: from abc import ABC, abstractmethod
  from typing import Tuple
  import torch
label_desc: Use image node_id 1 for calling the EpsScaling obj's underlying member method code with example usage: obj.__call__(sigma) and returns: c_skip,  c_out,  c_in,  c_noise